1

Introductions

Who you areWhere you’re fromWhat you tradeWhy you are hereWhat you wantOne fun thing

2

Finding Your Sweetspot

Self

System

Market

Self

System

Market

Get aligned

Stay aligned

3

Alignment in Action

Self

System

Market

ResultsResults

PurposePurpose ValuesValues BeliefsBeliefs ActionsActions

identity feelings thoughts behavior

Passion

Trading body of knowledge

4

Long term investing• Blended Monthly Rebalancing• Monthly rebalancing• Quarterly rebalancing• Annual rebalancing

Swing trading• Channeling• Overreaction• Triple screen• 551w• Washout• MaxPain Range Compression• Autoframing

Intraday trading• Frog (3)• RFA• RLCO• SQC

Techniques & concepts• Technical analysis• Statistics• Market classification• Position sizing• Trade framing• Core & Turbo• Green, Yellow, Red zones• Stalking and re-entry• Rangestat, slope stat, volstat• SQN and TQN

Systems

Strategies

Techniques

Tips

Core & turbo Core & turbo

Material framework

5

Market

Core

Swing

Day

Techniques

&

Tips

Self(Psychology, learning style, objectives, skills, risk)

6

System ASystem A

System BSystem B

Monthly RBMonthly RB

OverreactionOverreaction

5DD5DD

Max PainMax Pain

Triple screenTriple screen

WashoutWashout

ChannelingChanneling

%

%

System BCan be a screen

or set-up for System A !

System BCan be a screen

or set-up for System A !

Growing the tradeGrowing the trade

2-10 days

7

8

Beliefs about Self

10

Bias

Self-attributionSelf-attribution

Knowledge illusionKnowledge illusion

Illusion of control

Illusion of control

Biased 2d hand knowledge

Biased 2d hand knowledge

Hindsight biasHindsight bias

Confirmation biasConfirmation bias

Illusory correlationIllusory correlation

Overconfidence, Optimism bias

Overconfidence, Optimism bias

Illusory trends & patternsIllusory trends & patterns Sample sizeSample size

Representativeness heuristic bias

Illusion

of

Validity

Illusion

of

Validity

11

The inside of my head is a busy place

Chief

of

StaffStaff Call

System

A

System

B1

CEO

System

B2

System

B3

System

B4

System

B4

R&DCust

SvcTrading Prototype Accting Benchmark

Production

13

14

Systems Beliefs

15

The Trading System & Plan

Trading Systems

Market filter

Setup conditions

Entry signal

Protective Stop

Re-entry strategy

Exit strategy

Position sizing algorithm

Trading SystemExecutive summary

Business description

Industry overview

Competition

Self Knowledge

Trading Strategy

Beliefs, alliances, coaching

Trading edges

Financial Info

Contingency planning

16

Beliefs about SystemsA group of components organized to seek a goal in an environment

• Purpose (Objectives)• Whole > Sum of parts• Input-Process-Output• Interactive, Integrative, Iterative• Feedback loops and learning: Relationships• Reinforcing and counterbalancing• Boundaries and durations: Scope• Non-linear, dynamic relationships• Modeling and describing is learning• Hard, Soft, Evolutionary systems• The Map is not the territory, but it can help

Input Process

Environment

Output

17

Beat the marketHighest return within risk toleranceAchieve required return at the lowest riskUnit of return vs unit of riskLongevity vs shortest time to achieve goalBe small when wrong, large when rightFeel professional (BE PROFESSIONAL)

Be careful what you ask for

Objectives

18

Monthly review questions

• What worked for my trading this past month? What did not work? • What do the metrics tell me - in what instruments did I make

money? In which did I lose? Is there a pattern? • Did I keep to my exercise and meditation schedules? • Was there a correlation between my trading and how I felt for that

day? • Did I monitor the Ebb & Flow position sizing or did I persist with

too large or too small a size even after market conditions changed?

• What were my greatest challenges/lessons? • Of what am I most proud? What do I most regret? • What attitudes and actions will I take with me into the new

month? What lessons have I learned this month? • What limiting beliefs did I shift? What negative emotions did I

shift? • How did I grow, improve, and expand myself?

19

Decision making systems

20

Oh! The Choices you’ll make!

Risks

Risk management

Trading vehicles

Trading systems

Trading strategies

Time Frames

Objectives

21

Market Beliefs

22

What’s the nature of the market?Description

Dynamic?

Process

Strategy

Process

Value

Simple RandomChaoticComplexComplicated

Closed, linear

Static

Instinct

Training

Analysis

Speed, precision

Closed, linear

Static

Rational

Engineering

Analysis

Control

(Closed), network

Dynamic

Systems

Adaptive

Modeling

Learning

Open, (network)

Dynamic

Morphing

Metaphorical

Balance

Sense-making

Probabilistic

Uncertain

Statistical

Analytical

Calibration

Discipline

• Different situations need different responses, strategies, approaches

• Boundaries, indicators, volatility?• What about the market?

23

Performance Math

Market

Sector

Stock

50%

25%

25%

24

Market Classification

BullBear

BullBear

Volatile QuietBullBear

Volatile QuietBullBear

Volatile Quiet

Bull

Sideways

Bear

Volatile Quiet

Bull

Sideways

Bear

Volatile Normal Quiet

Bull

Sideways

Bear

Volatile Normal Quiet

Bull

Sideways

Bear

2/3 1/61/6

25

Market classification strategyAverage of %gainmkttype TotalBear -0.136Bull 0.111Sideways -0.025Grand Total 0.039

Average of %gainVtype Total

1 0.0702 0.0363 0.012

Grand Total 0.039

Average of %gainCtype Total

11 -0.119 Bear Quiet12 -0.135 Bear Normal13 -0.145 Bear Volatile21 0.067 Sideways Quiet22 -0.031 Sideways Normal23 -0.073 Sideways Volatile31 0.101 Bull Quiet32 0.107 Bull Normal33 0.144 Bull Volatile

Grand Total 0.039

SPY Volatile Normal QuietBull 0.144 0.107 0.101

Sideways -0.073 -0.031 0.067Bear -0.145 -0.135 -0.119

Notes:• SPY = mkt• 13 years, daily data• Bull vs Sideways vs Bear• Volatile vs Normal vs Quiet• Examine each axis• Combine into 3x3 matrix• Examine slope of 50d MA too• Very interesting results

Notes:• SPY = mkt• 13 years, daily data• Bull vs Sideways vs Bear• Volatile vs Normal vs Quiet• Examine each axis• Combine into 3x3 matrix• Examine slope of 50d MA too• Very interesting results

quietnormalvolatile

26

Market condition

• Bull

• Sideways

• Bear

• Quiet• Normal• Volatile

27

Market condition

• Bull

• Sideways

• Bear

• Quiet• Normal• Volatile

• 5DD & 5DDC

• ETF2

• ETF C

• WO & WOC

• ETF O

• 5DD & 5DDC

• WO & WOC

• 5DD & 5DDC & 5DDF

• WO & WOC & WO Failure

• ETF O

• Triple Screen

• Triple Screen

• 551w screen

• 551w screen

28

Mental Models

29

Sector Analysis

Large

Value Blend Growth

Medium

Small

The Morningstar Cube

30

Efficiency of Hierarchy

Mkt

Dow NASS&P

Companies

Sectors

Major Indices

Equity Mkt

S

B G

M

V

L

"Morningstar Cube"

Top-Down Approach

31

Mkt

Dow NASS&P

Companies

Sectors

Major Indices

Equity Mkt

S

B G

M

V

L

"Morningstar Cube"

Investor

Management Lens/Filter(provided by fund managers)

Top-Down Approach

Efficiency of Hierarchy

32

Liquid US Index ETFs: Can be shorted on a downtick

DIA SPY QQQ

IJJ MDY IJK

IJS IJR IJT

Value Blend Growth

Large

Mid

Small

World Market Model

33

34

Equities

Beliefs

Real Estate Business

Stormy Weather

•Results•Losing Streaks•Experts•Advertising•Media•Self-doubt•Emotions•Success•Guilt

Statistics

35

36

Traffic lighting with statistics

Average

+1 St Dev

-1 StDev

Adaptive

Time period matters

Current state

Changing state

Time series

Adaptive

Time period matters

Current state

Changing state

Time series

37

Extremes

1/6 1/6

worst best

2/3

normal

38

Getting on the bandwagon

Innovators

Early adopters

Early mass adopters

Late mass adopters

“Grumpy old men”

1

2

4

3

51

2

4

3

5

0%

100%

50%

Systems

39

Systems and timeframes

40

Frequency StrategyAnnual Annual passive

Quarterly Quarterly momentumMonthly Monthly momentumWeekly x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x ETF2

ChannellingOverreactionTriple Screen5DDWashout551wRFA30-60 qualityMaxPainModified French MoIndex RSHedged index pairs

Opportunity & Patterns

121 2 3 4 5 6 7 8 9 10 11

1Decision timing

1 2 3 4

Example of Green & Yellow Zone

41

Mechanical entry for the swing trade

Profit target for the swing trade

Standard frame

Initial stop for the swing trade

Yellow zone

Red zone

When the swing trade pattern fired

Green zoneI want to be long in the swing trade position

I can try to front run a green zone trade if I can see to the one inside yesterdays range

I am out of the swing trade or I am going short, because it’s failing

42

Green Zone Trading:

mechanical trading once Price moves above yesterday’s range

• Use scans & systems to find high probability/high payoff swing trade candidates

• Any of the Tortoise swing trade systems, patterns, preferences• Frame the trades that meet 2:1 reward:risk ratios on a re-test of the

10day High• Enter the trades when price > yesterday’s high +.05• Initial risk: .05 below yesterday’s low (or 1x ATR if you prefer)• Once in the trade, use a trailing stop of the initial risk or adjust to .05

below yesterday’s low

Think of the Green Zone as the Core position with overnight/Swing trade levels of risk

Green zone & Yellow zone trading

Green zone & Yellow zone trading

43

Yellow Zone Trading:

intraday opportunity trading on a mechanical trade, with tactical momentum

• Start with any Green Zone trade frame that gives 2:1• Look for opportunities when you can see 2:1 reward:risk, using the

mechanical entry as your profit target• Tighten up your stop and prepare to take profits if it stalls near the

mechanical entry• Consider adding another position at the mechanical entry, or simply

accept the current trade as your mechanical Green Zone trade, but with an improved entry, and let it become your swing trade

• If you have a successful Yellow Zone trade AND a Green Zone trade, take the Yellow Zone trade off before the close, so you only carry the swing trade risk overnight, then seek to get back in the following day with another Yellow Zone trade

Think of the Yellow Zone as the “Turbo” position with intraday trade levels of risk

44

Green zone & Yellow zone trading

Any swing pattern can get us here

How to think about trading the “Gap fail”

ETF 2

45

“I got 3% return,

is that any good?”

47

comparing a range of performancecomparing apples and oranges"normalizes" data, helps trendspotting

(x-min)

(max-min)100 *

3- (-5)

15- (-5)= 40100*

3- (-12)

8- (-12)= 75100*

Indexing

15 813 511 36 23 12 01 -4-3 -6-4 -8-5 -12

48

Top Down analysisMarket ConditionETFsRegions

Top Down analysisMarket ConditionETFsRegions

CalculationsStrengthConsistencyQualityAsset allocation

CalculationsStrengthConsistencyQualityAsset allocation

ReportsBenchmarkingETF "stars"RegionsETF swing trading

ReportsBenchmarkingETF "stars"RegionsETF swing trading

GoalsConsistencyDisciplineRoutineSimplicity

GoalsConsistencyDisciplineRoutineSimplicity

ETF 2.0 summary

49

ETF 2.0StrengthAverage QualityConsistency ++=

Strength: calculate RS (blended 3 & 6 month performance) 0-100 STR

Consistency: indexed, 10 week weighted average of Relative Strength 0-100 CON

Quality: indexed, 40 week “Quality rating” (Avg%Gain) / (StDev) 0-100 QUAL

Average: the average of STR + CON + QUAL 0-100 AVG

Strength: calculate RS (blended 3 & 6 month performance) 0-100 STR

Consistency: indexed, 10 week weighted average of Relative Strength 0-100 CON

Quality: indexed, 40 week “Quality rating” (Avg%Gain) / (StDev) 0-100 QUAL

Average: the average of STR + CON + QUAL 0-100 AVG

50

ETF 2.0 assessment (2005-2007)

Ruleset observations

1. Outperforms SPY buy and hold

2. Outperforms SPY timed buy & sell

3. Timing adds value

4. Selection adds value

5. dB finds every trend, long and short, supports opportunity trading as well as weekly positioning

6. Exits• 10% stops are good for starting, but could be tightened on

winners and in Bear markets• Strong argument for 3-4R winner as a Good Win to protect• Stronger argument for 5R winners as Exceptional win

Avg loss: 5%

1R = 5%

51

ETF 2.0 assessment (adds 2008-2010)

Ruleset observations

1. Outperforms SPY buy and hold, timed buy and sell

2. Timing, selection adds value

3. dB finds every trend, long and short, supports opportunity trading as well as weekly positioning

4. Replace Tortoise Index with 6 month RS (easier)

5. Max drawdown -8% in 2 bear markets (SPY -43%)

6. Exits• 10% stops are good for starting, but could be tightened on

winners and in Bear markets• Strong argument for 3-4R winner as a Good Win to protect• Stronger argument for 5R winners as Exceptional win

Avg loss: 5%

1R = 5%-3 -2 -1 0 1 2 3 4 5 6 7 8 9

0

10

20

30

40

50

60

70

80

Fre

qu

en

cy

R Multiple

ETF2 ETF2 SPY

number 265 942

max 7.28 7.26

average 0.19 -0.003

min -2.54 -4.92

stdev 1.73 0.870822

SQN 1.098266 -0.03445

T Score 1.787847 -0.10573

Index Overreaction

53

Index Overreaction

Strategy: Main indexes only Trade only with the long term trendSignificant short term move away from the trend.Short term trade to capture the snap back

Key Concepts: ATR % defines significant move200d MA = long term trend10d MA = short term trendVolatile move away from the short term trendSnap back to short term trend usually "over-corrects"

54

Index overreaction

Profitable every year from 1994 to 2004SPY, QQQQ, MDY, IWM, SMH

Made money in both bull and bear marketsSimple to trade and easy to learnmechanical systemConsistent money maker on long & short sideOutperformed buy and holdA few simple rules, 5 minutes a day or less to implementStatistics based entry, based on volatility (dynamic)

Concept: the market corrects after a significant overreaction away from the trend

55

Overreaction: Buys

# Rule Comment1 Today's close >200d SMA Trade with dominant LT trend

2 Today's High < 10d SMA Pullback from main trend

3 Today's Close 1x ATR%< 10d SMAStrong move beyond normal volatility levels

4 Buy at the close (or tomorrow's opening) Close is preferred

5 Buy another unit if setup conditions repeat while you are in the trade

6 Exit at today's close when yesterday's close is > 10d SMA

Catches the overreaction snapback

ATR%(14) = a measure of short term volatility

56

Overreaction System Rules: Sells

# Rule Comment1 Today's close <200d SMA Trade with dominant LT trend

2 Today's High >10d SMA Pullback from main trend

3 Today's Close is at least 1x ATR% >10d SMA

Strong move beyond normal volatility levels

4 Sell at the close (or tomorrow's opening) Close is preferred

5 Sell another unit if setup conditions repeat while you are in the trade

6 Exit at today's close when yesterday's close is < 10d SMA

Catches the overreaction snapback

57

Index Overreaction System summary

CommentsDon’t need to monitor all dayTakes advantage of long and short sidesCash is not tied upCan calmly enter the market in the currect direction in emotionally

challenging marketsMechanical signals don't require discretionary judgementHigh percentage of winning trades

ApplicationTrade a basket of ETFsKeep it simple and emotion free: apply the rulesPaper trade until you are comfortableTrade small position sizes

Index Channeling

59

Channeling: Buys

# Rule Comment1 Today's close >200d SMA Trade with dominant LT trend

2 Today's Close < -80 Williams%R (10) Pullback from main trend

3 Buy at the close (or tomorrow's opening) Close is preferred

4 Buy another unit if setup conditions repeat while you are in the trade

5 Exit at today's close when today's close is > -30 Williams%R

Catches the overreaction snapback

Williams%R (10) = a measure of short term overbought/oversold

60

Overreaction/Channelling Stops

Considerations:• 3% trailing stop for broad US indices• 5% trailing stop for IGW + international broad

indices

61

5 days down(5DD)

62

5DD concept

63

1 2 3 4 5 6 7 8 9 10123456789

10

test:mkts:time: 10 year backtest: 1996-2006

days to hold

days down in

a row

5DD concept

Dow, S&P, NAS100; ETFsBull, Bear, Sideways, All

551w

64

“551w”…where do ideas come from?

65

Mastermind effect

Day 2, morning break…Ken & Leo Willert

(in between talking about drumming)

Component analysis: 5 weeks up is favorable… 5 days down is favorable… 1 day up is favorable … Universal Entry (consistency, risk mgt) Williams %R <-50 (profitable swing)

“551w”

66

Draw a concept diagramUse the “framing” structure

“551w” concept diagram

67

“551w” concept diagram: a way

68

Washout

69

70

Washout Pattern

What if everything you knew was wrong?

“It’s not what you don’t know,

it’s what you know that ain’t so”

-Harry Truman

71

You trade your beliefs

Conventional Wisdom

• Ride the trend• Strongest sectors• Strongest stocks• You can’t pick

bottoms• Buy them when

they hate them• Have the courage of

your convictions• Small caps

outperform

What If?• Avoid the trend• Weakest sectors• Weakest stocks• Pick bottoms• Buy them when no

one cares• Be afraid of your

convictions• Focus on large caps

What would this look like?

72

Assertions

• Buy large cap, weak stocks when nobody cares• When everyone who was going to sell has sold• When there is price evidence of short term

improvement• Buy them when the market is going up• Buy them when they are going up and the

market is going down• Plan for the recent swing high• Maintain 2:1 reward:risk ratio• Cut at the first sign of hesitation • Watch for signs of institutional interest

73

Operationalize the beliefs

• OEX stocks (S&P 100)– (institutional $, risk mgt)

• Oversold on an annual basis (W%R(260) <-80)– Long term sellers have sold

• Oversold on a short term basis (W%R(10) <-80)– Short term sellers have sold

• 0• -20

• -50

• -80

• -100

74

Price patterns

The Big Sell

The swing low

Setup day 1 (S1)

Higher low

Close > open

Close > yesterday’s high

Entry day

On Price > S1 (High)

Setup Day

EntryDay

Exit

Entry

75

Reward: Risk

• Swing High

• Entry

• Exit

• ATR

• Trailing stop

• W%R(260) > -80• Institutional confidence

76

Slightly lower reliability• Lower average R win, SQN• More opportunities per week• Still tight risk controlled

Triple Screen System

Triple Screen System

variation on Dr Alexander Elder's system

79

Screen 1: Major Movement

Screen 2: Intermediate Movement

Screen 3: Timing

Find strong trendsApply an oscillator to daily chartUse daily declines suring weekly uptrends to find buying opportunitiesUse daily rallies during weekly downtrends to find shorting opportunities

Triple Screen Concept

80

Weekly trend Daily Trend Action OrderUp Up Wait NoneUp Down Go long Trailing buy stop

Down Down Wait NoneDown Up Go short Trailing sell stop

Screen 1: Major Movement

Screen 2: Intermediate Movement

Screen 3: Timing

Triple Screen Strategy Summary

81

0%

100%

50%

Thought experiment: if the pullback to the 20dMA = 10%,and Buffet suggests 5% per year in equities is good,then a 50% retracement = a 5% move in a few days,Is that enough? for a short term system?

Triple Screen Concept

82

ADX > 25, +DI > -DI orMACD-Hist uptick

Pullback to 20d MA or <-80 on Wlliams%R

Breakout higher high on hourly candlestick

• Min 2:1 risk/reward• Stop: low of entry day or previous day's low, whichever is lower• Ratchet the trailing stop to breakeven as soon as possible• Preserve 70% of profits of a 3R winner• or, manage exits with candlesticks

Triple Screen Concept

83

QQQQ

84

85

86

Daily ETF “Triple Screen” screen

Mastermind Insights

87

Supertrader Summit Insights

88

• Chatroom Mastermind effect• Feed the bulldog every day• Where do beliefs come from?• Connectivism & The Market Mosaic• Trader Quality Number• Your system is what you do• Double loop learning & learning styles, auditory learning• “That coal won’t shovel itself”• Tell the Universe• All your preparation is for…• Phase transitions and critical states• Zeno stop• Trade framing• Snapping turtle• 551w• “.25R improvement on every trade”• Zero state• Ready - Fire - Aim• You are ALWAYS trading

Trade Index

Analysis

90

The LeBeau Stop Quality Index

• From the Systems seminar 1996:• Time in trade = t• Find best price in time = 2t• Your exit / Best Possible exit• A number between 0 and 1• .5 is really good• My refinement: consider time value of money• Spreadsheet implementation with XLQ

91

Trade Index AnalysisProcedure:

Calculate the length of your trade (t)

Find the best possible exit during time period (2t)

Divide Actual/Best Possible to find Exit Efficiency

Scale: 0 <-> 1.0

Procedure:

Calculate the length of your trade (t)

Find the best possible exit during time period (2t)

Divide Actual/Best Possible to find Exit Efficiency

Scale: 0 <-> 1.0

Notes:

Can only examine Wins vs wins

Must do separate calc for comparing efficiency of Losing trades

Does not consider time value of money (gain/time)

Notes:

Can only examine Wins vs wins

Must do separate calc for comparing efficiency of Losing trades

Does not consider time value of money (gain/time)

Entry

Time (t)

Exit

Actual Gain (g)

Best Possible Exit

Best Possible Gain (b)

Time (t)

Lebeau Exit Efficiency = Actual Gain / Best Possible GainLebeau Exit Efficiency = Actual Gain / Best Possible Gain

92

Trade Index Analysis

Notes:

By inspection you can see that the actual exit is very good compared to Best Possible Exit #1

Best Possible Exit #2, though is best of all because you get maximum gain AND your money available quickly for the next opportunity

Gain/Time may matter if you have a system with relatively short holding periods and many opportunities

Notes:

By inspection you can see that the actual exit is very good compared to Best Possible Exit #1

Best Possible Exit #2, though is best of all because you get maximum gain AND your money available quickly for the next opportunity

Gain/Time may matter if you have a system with relatively short holding periods and many opportunities

Entry

Time (t)

Exit

Actual Gain (g)

Best Possible Exit #1

Best Possible Gain (b)

Time (t)

Lebeau Exit Efficiency = Actual Gain / Best Possible GainLebeau Exit Efficiency = Actual Gain / Best Possible Gain

Best Possible Exit #2

93

Trade Index AnalysisThought experiment:

Think of your ruleset for filters, screens and entries as a lens that waits to see the market in a certain condition that you have determined is favorable for a trading system

Suppose you have developed an exit strategy that results in a positive expectancy system, and that through a combination of backtesting, prototyping with small position size, and finally trading with normal risk, you are satisfied that the system is robust

How can you determine if your rule set is “in tune” with the market condition? How will you make sure you are not missing other, easier opportunities?

Note: this is hard to do especially if your system has a positive expectancy!

Thought experiment:

Think of your ruleset for filters, screens and entries as a lens that waits to see the market in a certain condition that you have determined is favorable for a trading system

Suppose you have developed an exit strategy that results in a positive expectancy system, and that through a combination of backtesting, prototyping with small position size, and finally trading with normal risk, you are satisfied that the system is robust

How can you determine if your rule set is “in tune” with the market condition? How will you make sure you are not missing other, easier opportunities?

Note: this is hard to do especially if your system has a positive expectancy!

MarketA complex adaptive system

MarketA complex adaptive system

stalking stalkingtrade

entry exitruleset

94

Trade Index AnalysisProcedure:

For each trade, calculate the time in the trade as (t)

Find the Highest High and Lowest Low in time period 2t

Index the distance between Highest High and Lowest Low on a scale of 0-100

For each trade, calculate and Entry Index, Exit Index, and Trade Index

Calculate an Average for the Entry Index, Exit Index and Trade Index

If the Average Entry Index >70, the Average easier, larger opportunity is to the short side (even though you may have a positive expectancy system going long)

Procedure:

For each trade, calculate the time in the trade as (t)

Find the Highest High and Lowest Low in time period 2t

Index the distance between Highest High and Lowest Low on a scale of 0-100

For each trade, calculate and Entry Index, Exit Index, and Trade Index

Calculate an Average for the Entry Index, Exit Index and Trade Index

If the Average Entry Index >70, the Average easier, larger opportunity is to the short side (even though you may have a positive expectancy system going long)

Highest High

Lowest Low

trade

entry

exit

100

0 0

100

Trade Index

1R

Time period (t)Time period (t)

95

Trade Index Analysis• Procedure: • For each trade, calculate the time in the trade as (t)• Find the Highest High and Lowest Low in time period 2t• Index the distance between Highest High and Lowest Low on a scale of 0-100• For each trade, calculate and Entry Index, Exit Index, and Trade Index• Calculate an Average for the Entry Index, Exit Index and Trade Index• If the Average Entry Index >70, the Average easier, larger opportunity is to the short

side (even though you may have a positive expectancy system going long)

• Procedure: • For each trade, calculate the time in the trade as (t)• Find the Highest High and Lowest Low in time period 2t• Index the distance between Highest High and Lowest Low on a scale of 0-100• For each trade, calculate and Entry Index, Exit Index, and Trade Index• Calculate an Average for the Entry Index, Exit Index and Trade Index• If the Average Entry Index >70, the Average easier, larger opportunity is to the short

side (even though you may have a positive expectancy system going long)

• Highest High

• Lowest Low

• Average trade

• entry

• exit• 100

• 0 • 0

• 100

• Trade Index

• 1R

• Time period (t)• Time period (t)

• Opportunity!?• Opportunity!?

96

Trade Index Analysis• Procedure: • For each trade, calculate the time in the trade as (t)• Find the Highest High and Lowest Low in time period 2t• Index the distance between Highest High and Lowest Low on a scale of 0-100• For each trade, calculate and Entry Index, Exit Index, and Trade Index• Calculate an Average for the Entry Index, Exit Index and Trade Index• If the Average Entry Index >70, the Average easier, larger opportunity is to the short

side (even though you may have a positive expectancy system going long)

• Procedure: • For each trade, calculate the time in the trade as (t)• Find the Highest High and Lowest Low in time period 2t• Index the distance between Highest High and Lowest Low on a scale of 0-100• For each trade, calculate and Entry Index, Exit Index, and Trade Index• Calculate an Average for the Entry Index, Exit Index and Trade Index• If the Average Entry Index >70, the Average easier, larger opportunity is to the short

side (even though you may have a positive expectancy system going long)

Highest High

Lowest Low

Washout

100

0 0

100

Time period (t)Time period (t)

4854

70

5DD44

97

Applying Exit Efficiency

Average of Xndxtype Total5DD 0.505DDC 0.84ETF2 0.59ETFR 0.63ETFV 0.53TS 0.69WD 0.65WO 0.65WOC 0.70WW 0.61Grand Total 0.60

type Ticker Entry Date Entry Price Exit Date Exit Price t 2t# 2tdate hiPrice loPrice XndxETFV MDY 1/4/2005 117.25 1/19/2005 117.07 15 30 2/18/2005 122.38 115.15 0.266ETFV DIA 1/5/2005 105.71 1/19/2005 105.25 14 28 2/16/2005 108.68 103.62 0.322ETFR QQQQ 2005-06-14 37.58 2005-06-16 37.90 2 4 6/20/2005 38.21 37.25 0.677

TS LQD 2005-11-07 106.50 2005-12-01 107.70 24 48 1/18/2006 108.65 106.07 0.632TS OIH 2005-11-07 116.00 2005-11-28 125.00 21 42 1/9/2006 140.29 112.6 0.448TS NGS 2005-11-10 21.30 2005-11-25 21.50 15 30 12/25/2005 25.88 15.67 0.571

WW LUV 2006-02-01 16.55 2006-03-14 17.75 41 82 6/4/2006 18.2 15.28 0.846WD QQQQ 2006-04-13 42.10 2006-04-17 41.80 4 8 4/25/2006 42.82 41.39 0.287WD RWR 2006-03-06 74.50 2006-03-15 77.00 9 18 4/2/2006 79.3 74 0.566

ETF2 EWD 2006-09-05 26.45 11/28/2006 29.30 84 168 5/15/2007 33.4 25.51 0.480WO BOL 2006-04-18 48.00 2006-04-25 50.00 7 14 5/9/2006 50.39 40.75 0.960

98

Technique

99

100

5 day Slope of the 50d MA

Average of %gainslopetype Total

0 0.0221 0.047

Grand Total 0.039

Notes:SPY = mkt; 13 years, daily data

All great bull mkts began when slope of 50d MA was flat or positive

Sometimes positive slope was false

Takes 3-4 weeks after a Bear to get slope back to flat

How to measure?

Very interesting results

Notes:SPY = mkt; 13 years, daily data

All great bull mkts began when slope of 50d MA was flat or positive

Sometimes positive slope was false

Takes 3-4 weeks after a Bear to get slope back to flat

How to measure?

Very interesting results

50day MA slope

A trend in transition

101

System Quality Number application

• Apply the concept of System Quality number to the daily output of “black boxes” called stocks and ETFs

• My implementation:– 10 x (AvgGain%(t))/(StDev(t))

• Uses:– Q40 for NLNTF funds: t= 50 weeks– ETFs/large caps: t = 30,60,90,200 days

• “A way” to quantify “efficiency & effectiveness”

102

The Universal Entry

• After a successful trade, whose exit was triggered by selling, I look for a re-entry using the Universal Entry (UE)

• After the sell day which triggered the exit, buy today if:• Open inside yesterday’s real body• Price 5 cents higher than yesterday’s high• Use a stop loss of:

• 5 cents below yesterday’s low, • ½ ATR, trailing (more aggressive)

• In a Washout Continuation pattern, this will often convert to a long term trend following trade, with an initial profit target of the 200d MA, and then beyond

1. The Big Sell Day(s)

2. The Swing Low Day

3. The Setup Day

4. The Entry Day

5. The Successful Trade Day(s)

6. The Sell Day

7. The Continuation Entry Day

1

2

6

4

3

7

5

The Universal Entry

103

Risk Management

104

DiversificationPosition sizingPortfolio heatBenchmarkingSystem tradingObjectivesRisk toleranceExpectancyMA of equity20 trade MA of expectancyFundamentalsExtreme valueAssume you are wrong until the mkt proves you right

DebriefingTrading planBusiness planAfter action reviewsSystem of systems

Risk management

105

Market Assessment

106

How much of the portfolio?

Set-upStalking

Entry

Initial exitCapital preservation

Profit preservation

ExitProfit target?

$/share

$/share

Reward

Risk

How do you decide?

How do you decide?

Position Sizing

Exercises

107

108

How do you feel about these charts?

• Like/dislike?• Long vs Short vs Stand Aside?• What will it do next?

109

1

110

2

111

3

112

4

113

1

43

2

114

115

Which system would you trade?

• Long term trend following system• Returns 30% per year, 1 opportunity/yr

• Swing trading system• 60% winners, averaging 2 R• 40% losers, averaging -1R• Trades last a week, on average• 3 trading opportunities per week

• At what risk level does A = B? (bonus)

Range Stat

AA case study example of rangestat

AA intraday range stats

+4%

+2%

+6%

-4%

-2%

-6%

closeopen

Yesterday’s candle

close

Intraday moves

Max 12.36%+1SD 5.28%Avg 3.50%

-1SD 1.71%Min 1.20%StDev 1.79%

Max 11.43%+1SD 2.49%Avg 1.46%-1SD 0.44%Min 0.39%StDev 1.03%

Intraday moves

• AA intraday range stats

• +4%

• +2%

• +6%

• -4%

• -2%

• -6%

• close• open

• Yesterday’s candle

• close

• Normal moves will range between 2 and 6% intraday

Max 11.43%+1SD 2.49%Avg 1.46%-1SD 0.44%Min 0.39%StDev 1.03%

Intraday moves

AA intraday range stats

+4%

+2%

+6%

-4%

-2%

-6%

closeopen

Yesterday’s candle

close

Normal moves will range between 2 and 6% intraday

Max 11.43%+1SD 2.49%Avg 1.46%-1SD 0.44%Min 0.39%StDev 1.03%

Intraday moves

AA intraday range stats

+4%

+2%

+6%

-4%

-2%

-6%

closeopen

Yesterday’s candle

close

Normal moves will range between 2 and 6%intraday

Max 11.43%+1SD 2.49%Avg 1.46%-1SD 0.44%Min 0.39%StDev 1.03%

Intraday moves

AA intraday range stats

+4%

+2%

+6%

-4%

-2%

-6%

closeopen

Yesterday’s candle

close

Normal moves will range between 2 and 6%intraday

AA: trading at $13 2% = $0.25, 4% = .5, 6% = .75

If you can manage a .1 iStop, the normal intraday move = 5R

Hypothetical trade frame

Max 11.43%+1SD 2.49%Avg 1.46%-1SD 0.44%Min 0.39%StDev 1.03%

Intraday moves

• AA intraday range stats

• +4%

• +2%

• +6%

• -4%

• -2%

• -6%

• close• open

• Yesterday’s candle

• close

• Normal moves will range between 2% and 6% intraday

• AA: trading at $13 2% = $0.25, 4% = .5, 6% = .75• If you can manage a .1 iStop, the normal intraday move = 5R

• Know your target• Know the potential• Know what’s normal• Control your risk• Be surprised into catastrophic success

125

• Who are you?• What are you trading today?• Finalize your trading plan• Brief overview of your strategy for the day

• Use your trade log, document trades• Take screen shots of frames/entries/decisions/exits (case study)• 1 member of the group monitor SPY//try to trade SPY (virtually)• “Attention on Deck” if you see something or have an observation• Every 30 minutes we will summarize

Logic chain

126

• i start with SPY to assess mkt conditions from the open and during the day

• i compare the vertical column above and below for intraday relative strength comparisons of indices and sectors to SPY

• if a sector looks very good or very bad i then go east and west to find an even better target for easy trading

• to include looking all the way to the right for stocks outperforming their peers in an outperforming sector, going in the same up direcition as mkt

• if mkt failing i find worst sector ETF and trade the double inverse "long“

• the stocks and ETFs on there are often the result of swing trade patterns which are favorable for the next couple days so i have extra protection when trading them intraday

• the end

127

Research Program

128

Multivariate world market correlation model

Underlying causal model“competitive themes”

“hidden dynamic order”

GeographicUSJapanEuropeAsiaEAFE (not US)Latin AmEmerging Mkt

Business sectorUS sectors (SPDR list)Global sectors (list)

StyleValueBlendGrowthIndependent

Market CapLargeMediumSmallMicro

Asset classEquitiesReal estateBond/incomeCommodities

CurrencyUSDEuroYen

Themes & dimensions

Notes:• The themes compete to be the dominant driver of world market returns (a mix at any moment)• The dimensions compete within each theme for dominance (a mix at any moment)• There is a time component for dominance that may vary by theme and dimension • There is an “expected” duration and strength of dominance unique to each theme and dimension• Successful strategies could include the right mix of themes and dimensions in the portfolio• Monitoring “state” and context permits “planting” and “harvesting” according to the season

Notes:• The themes compete to be the dominant driver of world market returns (a mix at any moment)• The dimensions compete within each theme for dominance (a mix at any moment)• There is a time component for dominance that may vary by theme and dimension • There is an “expected” duration and strength of dominance unique to each theme and dimension• Successful strategies could include the right mix of themes and dimensions in the portfolio• Monitoring “state” and context permits “planting” and “harvesting” according to the season

%return%variation

%return%variation

Information:• Fundamentals• Technical• Seasonality• Productivity• Employment• Consumption• Policy• Business cycle• Theories• Results• Memory

Information:• Fundamentals• Technical• Seasonality• Productivity• Employment• Consumption• Policy• Business cycle• Theories• Results• Memory

Actors & agents• Liquidity• Time horizons• Required returns• Risk tolerance• Psychology• Analysis• Feedback• Strategies

Actors & agents• Liquidity• Time horizons• Required returns• Risk tolerance• Psychology• Analysis• Feedback• Strategies

Market competition

Questions• What’s working?• What was working?• What’s starting to work?• What’s starting to lose?• What’s the context?• Frequency & amplitude?• Best heuristics now?• Confidence?

Questions• What’s working?• What was working?• What’s starting to work?• What’s starting to lose?• What’s the context?• Frequency & amplitude?• Best heuristics now?• Confidence?

129

Forecasting model committee

StatisticsMultivariatePrinciple ComponentsEbbs and FlowsDynamic

Model base

Tortoise 2.0Short termRS & volatility8-10 winnersSector, region limits

Business forecast Internal model baseData pattern drivenAlgorithm selectionCompetition winner

Monte Carlo 10 year, monthly %Mean reversionPerformanceVolatility

Rules basedHybrid, short termLinear regressionMarket conditionRegional focus

Neural Network Monthly predictionWeekly prediction“Black Box”Expert architecture

CART ClassificationRegression TreeNon-linearExplanatory power

Momentum Fama 12 month rulesST momentumIT momentumLT momentum

Annual Rebalance10 sectorsJanuary rebalanceNo timingLong only

Buy & HoldTotal Market IndexBaseline

Model Predictions

HistoricalPerformance

AnalysisAssessment

Strategy Selection

PerformanceAssessment

StrategyAssessment

Lessons Learned

%return & %variationOf Models & System

%return & %variationOf Models & System

Model forecasts Model preferences

Model forecasts Model preferences

Price basedModel-specific time frame

Price basedModel-specific time frame

Rules for combining Rules for weighting

Rules for combining Rules for weighting

Compare & contrastAgreement, disagreement

Compare & contrastAgreement, disagreement

Rules & decisionsModel performance

Rules & decisionsModel performance

Evaluate System rulesApply learning

Evaluate System rulesApply learning

Each decision cycle

130

World Market Model: Directed Acyclic Graph (DAG) Diagram

%return%variation

Region

GeographicUSJapanEuropeAsiaEAFE (not US)Latin AmEmerging Mkt

Business sectorUS sectors (SPDR list)Global sectors (list)

StyleValueBlendGrowthIndependent

Market CapLargeMediumSmallMicro

Asset classEquitiesReal estateBond/incomeCommodities

CurrencyUSDEuroYen

Themes & dimensions

Currency

Global sector

Style

Mkt Cap

Asset Class

US Sector

131

ETF components

VTITotal Mkt Index

VTITotal Mkt Index

US Business sector

Global Business sector

Asset classes

Regions

Currencies

Style

Capitalization

Live Trading

Stats

Live Feb 2011, day 1

133

R % avgmax 6.3 total 98 0.18min -3 win 48 48% 1avg 0.18 scratch 2 2% 0totalR 18 loss 48 48% -0.7avg win 1.0avg loss -0.7 sqn(10) 1.31sd 1.3 sqn(n) 1.30sqn 1.40

Live Feb 2011, day 2

134

net 14.88avg 0.132857stdev 0.998538SQN(10) 1.330517

win 59 52.7% 1.01 lose 53 47.3% (0.65)

Live Feb 2011, day 3

135

sum 25.76 win 47.1% 1.12 avg 0.22 scratch 6.7% 0

stdev 1.07 lose 45% (0.64) sqn 2.03

Live Feb 2011, day 4

136

Live Feb 2011, day 5

137

Live Trading

Prep

Example of Green & Yellow Zone

139

Mechanical entry for the swing trade

Profit target for the swing trade

Standard frame

Initial stop for the swing trade

Yellow zone

Red zone

When the swing trade pattern fired

Green zoneI want to be long in the swing trade position

I can try to front run a green zone trade if I can see to the one inside yesterdays range

I am out of the swing trade or I am going short, because it’s failing

Daily Trading Plan Notes (a way)

140

O 5DD O Long term

O WO O Short term

O Triple screen O Gaps

O 551w O RangeStat

O Channel O Pivots

O Overreaction O

O MaxPain O Regions

O MinPain O SPDRs

O French Mo O Mkt Cap

O 30-60 QD O Style

O Sector rotation O Countries

O O

O MaxPain

O MinPain

O French Mo

O 30-60 QD

O Open trades

O

Daily trading Plan Notes:

Sector Notes

Market condition

Notes

Styles

Continuations

Patterns

Daily: Plan-Prepare-Execute

141

142

Max(ever)

Min(ever)

Min(future)

Max(future)

Max(x)

Min(x)

Avg(x)

Avg+1SD(x)

Avg-1SD(x)

SD

SD

30 days of data

Calculate daily Ranges

Calculate statistics:• Max• Min• Avg• SD• Avg +1SD• Avg -1SD

• Calculate• Rstat / SD

• Select targets

• Stalk entry• Wait 30 min

HOD

Range

Stat

LOD

SD

SD

• 143

SPY

EFA

QQQQ

MDY

XLE

EWZ

ILF

EPP

MVVMZZ

UWM

QLD

IWM

QID

TWM

EEMEEV

FXP FXI

EFU IEV

XLB

XLF

XLI

SKF

SMN

BAC

AXP

JPM

VOT

HPQ

CSCO

MSFT

AAPL

CLF

GLD

AA

SLV AGQ SLWZSL

GDX GDXJ

CVX

CAT

HD

EWM

XME

NFLX

DBA

DBC

WMTTLT

DVN

Logic chain

• 144

i start with SPY to assess mkt conditions from the open and during the day

i compare the vertical column above and below for intraday relative strength comparisons of indices and sectors to SPY

if a sector looks very good or very bad i then go east and west to find an even better target for easy trading

to include looking all the way to the right for stocks outperforming their peers in an outperforming sector, going in the same up direcition as mkt

if mkt failing i find worst sector ETF and trade the double inverse "long“

the stocks and ETFs on there are often the result of swing trade patterns which are favorable for the next couple days so i have extra protection when trading them intraday

the end

The Curve

146

Consider the curve

• What do you see?

• What questions do you ask?

147

Consider the curve

• What do you see?• What else could it be?• Is this a belief or a prediction?• How else could you draw the curve?• What draws the curve?• Once drawn, is it static?• Where are you on the curve?• Where is the market?

148

Fair value

• On Average:• Where are you buying?• Where are you selling?

149

Slope?

• Slope?• Time period?• Normal?• Trend?• Duration?• Frequency & amplitude?

150

Fair value

• Slope?• Variation?• Stretch?• Normal?• Boundary of normal?

151

Market classification

• What are your measures?• What’s the time period?• How do you adapt?• Is there a larger time period slope at work?

• Bear?• Bear?

• Sideways?

• Sideways?

• Bull?

• Boundary conditions?

152

Market : Systems

• Where on the curve do your systems thrive?• Do you have systems for all regions on the curve?• Specialized systems vs general purpose systems?

• Bear?• Bear?

• Sideways?

• Sideways?

• Bull?

• 5DD• 5DDC • 5DD

• 5DDC

• 5DD• 5DDC

• ETF2

• WO• WOC

• WO• WOC

• ETF2

• ETF O

• ETF C• ETF O

• Triple• Screen

• Triple• Screen

• Triple• Screen

153

Attitude Checks

154

The analysts are crooks.The market makers were fishing for stops. I was on the phone and it collapsed on me.My neighbor gave me a bad tip.The message boards caused this one to pump and dump. The specialists are playing games.

It is my fault. I traded this position too large for my account size.It is my fault. I didn't stick to my own risk parameters.It is my fault. I allowed my emotions to dictate my trades.It is my fault. I was not disciplined in my trades.It is my fault. I knew there was a risk in holding this trade into

earnings, and I didn't fully comprehend them when I took this trade.

Attitude

155

Covey’s 7 Habits…for traders?!

• Be proactive• Begin with the end in mind• Do first things first• Think “Win/Win”• Understand, then seek to be understood• Synergize

• “Sharpen the saw”

• Continuous improvement

What is your totem animal?

156

•What does it mean to trade like a _______?•What qualities does __________?•What emotions? •What are the risks?•Where does it come from?•What does it represent?•How useful?

157

Stalking

• Not predicting• Knowing your prey• Identifying the patterns• Knowing the odds• Setting the conditions• Taking the shot

158

Bears go fishing

159

Lions await the herd

160

“YOU DON’T KNOW NOTHING”

161

Professional feelings• Calmness• Relaxation• a gentle pleasant humming in the background (Bach-like fugues)• crystal clarity on risk reward and my betting strategy• instant recognition of my strategy given my starting cards• an effortless ability to fold without regret• satisfaction with playing correctly when i call or raise and lose the hand

based on pot odds and strength of hand• there is an interesting feeling when i go all in for the right reason (based o

the odds and percent portfolio risk)• there is the same feeling (it feels like an octave lower, but still very

satisfying) when i make the right bet and the right play but for less than all in

• it is satisfying to have the feeling and the realization that i am in it for the long haul, and that i know i will endure by applying my rules, while acknowledging that sometimes you dont get the cards, but also knowing that risk management/position sizing will keep me in the game.

162

Let the course pick your club

• Master your tools• Pack your bag• Groove your swing• Know the course• Keep good score• Hit buckets of balls• Play your game• Breathe deeply• Enjoy the game• Leave it on the course

Technical Analysis

163

164

Traffic lighting with statistics

Average

+1 St Dev

-1 StDev

Adaptive

Time period matters

Current state

Changing state

Time series

Adaptive

Time period matters

Current state

Changing state

Time series

165

Extremes

1/6 1/6

worst best

2/3

normal

Technical Analysis ReviewAverage Directional Index (ADX)Average True Range (ATR)Moving Average Convergence/Divergence (MACD)Williams %R“NDX” (an improved Williams %R)Candlestick Charting200day MA “Stretch” %Slope of the 30d regression lineGap StatRange Stat

167

Getting on the bandwagon

Innovators

Early adopters

Early mass adopters

Late mass adopters

“Grumpy old men”

1

2

4

3

51

2

4

3

5

0%

100%

50%

168

Average Directional Index (ADX)(strength of trend)

Invented by Welles Wildermeasures strength of trendsimple but complex calculations measured on a scale of 0 – 100

low ADX value (generally less than 20) can indicate a non-trending market with low volumes

a cross above 20 may indicate the start of a trend (either up or down).

If the ADX is over 40 and begins to fall, it can indicate the slowdown of a current trend.

Can also be used to identify non-trending markets or a deterioration of an ongoing trend.

Although market direction is important in its calculation, the ADX is not a directional indicator.

169

ADX (continued)

Normal calculation: 14 day period with end of day dataADX >30 indicates there is a strong trendMomentum precedes price. When using ADX in your studies,

note that when ADX forms a top and begins to turn down, you should look for a retracement that causes the price to move toward it’s 20 day moving average (SMA).

In an up trending market, the technician will buy when the price falls to or near the 20 unit SMA, and in a down trending market, one should look to sell when the price rises to or near its 20 unit SMA.

ADX does not function well as a trigger. Prices will always move faster than the Average Directional Index, as there is too much of a smoothing factor, which causes it to lag the price movement.

If ADX goes below both DI lines, stop using trend following systems, as the market is choppy

ADX has been used in trading systems using +DI and -DI crossovers

170

ADX Caution

“Imagine that we have a nice long base. We jump on board when ADX starts rising from a low level. We successfully carry this trade all the way up to a high ADX level, somewhere above 30, and then the market turns down. The ADX will start to decline showing an absence of trending direction, but the price does not have an absence of direction, it is moving down!”

- Chuck LeBeau

171

ADX: the Formula

Calculating ADX is a two-step process. First, the difference of +DI and -DI is divided by the sum of +DI and -DI, and the quotient is multiplied by 100; the result is known as DX. Second, ADX is calculated by taking a modified moving average of DX.

Formula:DX = [ ABS( (+DI) - (-DI) ) ] / ( (+DI) + (-DI) )

ADX = modified moving average of DX

Where:n = number of periods+DI = current positive directional index-DI = current negative directional indexDX = current DX

172

ADX calculation

DX = +DI14 minus -DI14+DI14 plus -DI14

x 100 DI difference

DI sumx 100

ADX = Simple moving average of DX (14 = normal)

Inside day

Rising mkt

A

B

C

+DM

Outside day

A

C

B-DM

A

C

B

Zero DM

173

Trendspotting with ADX

174

Average True Range (ATR)(measuring volatility)

Average True Range ("ATR") is a measure of volatility. Introduced by Wilder in New Concepts in Technical Trading

SystemsCommon component of many indicators and trading systems.

Interpretation

High ATR values often occur at market bottoms following a "panic" sell-off.

Low Average True Range values are often found during extended sideways periods, such as those found at tops and after consolidation periods

175

ATR calculation

The True Range indicator is the greatest of the following:

The distance from today's high to today's low. ABS(A-B)The distance from yesterday's close to today's high.ABS (A-C)The distance from yesterday's close to today's low. ABS (C-B)The Average True Range is a moving average (typically 14-days)

of the True Ranges.

Rising mkt outside dayinside day

A

B

C

A

A

CC

B

B

176

MACD(Moving Average Convergence Divergence)

The MACD ("Moving Average Convergence/Divergence") is a trend following momentum indicator that shows the relationship between two moving averages of prices. The MACD was developed by Gerald Appel, publisher of Systems and Forecasts.

The MACD is the difference between a 26-day and 12-day exponential moving average. A 9-day exponential moving average, called the "signal" (or "trigger") line is plotted on top of the MACD to show buy/sell opportunities.

177

The 4 seasons of MACD-Histogram

178

Williams %R(a measure of overbought/oversold)

Commonly performed on a 10 day periodScale: 0 to minus 100 (can ignore the minus sign)0 to 20 considered overbought80 to 100 considered oversoldMust wait for price confirmation: a better setup than triggerUncanny in its ability to anticipate turning pointsFormula:

Highest High(n) - CloseHighest High(n)- Lowest Low (n)

x 100

179

Williams%R in action

180

10 NDX vs Williams %R

Williams %R10 NDX

0

-20

-80

-100

10080

200

uses current day data and previous 9readings are not intuitive

uses previous 10 days of datareadings are intuitiveextreme moves today are highlighted

181

Candlesticks Quicklook

Visually display much more info than bar chartsQuicker to identify important patterns than barsShould be used in conjunction with Western technicalsShould not be used on their own for entries or stand alone systemsDo not give price targetsReveal market psychology Tug of war between bulls and bearsCan signal change of trend or market pauses"Windows" or "gaps" are very powerful signalsLong shadows can identify support or resistance when taken in

combinationWork in multiple time framesGenerally well suited for intermediate and short term timeperiodsPay attention to Doji

182

Candlestick example

The highest price (upper shadow)

The opening or closing price, whichever is greater

The center ("real body")

The opening or close, whichever is less

The lowest price (lower shadow)

183

Candlestick examples

3 soldiers marching Long shadows (support)Triple cloud cover

HammerGravestone

Doji: indecisionEngulfing Evening star

184

Stretch above the 200d MA

Price

200d MA

Positive stretch

Negative stretch

• Where is it now?• What’s the most?• How does today compare?

• Where is it now?• What’s the most?• How does today compare?

200dMA % slope

185

186

200dMA stretch%: All indices

187

30day Regression line slope

188

189

Gap Stat

190

Range Stat

191

192