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1 Introductions  Who you are  Where you’re from  What you trade  Why you are here  What you want  One fun thing.

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Presentation on theme: "1 Introductions  Who you are  Where you’re from  What you trade  Why you are here  What you want  One fun thing."— Presentation transcript:

1 1 Introductions  Who you are  Where you’re from  What you trade  Why you are here  What you want  One fun thing

2 2 Finding Your Sweetspot Self System Market Self System Market Get aligned Stay aligned

3 3 Alignment in Action Self System Market Results Purpose Values Beliefs Actions identity feelings thoughts behavior Passion

4 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

5 Material framework 5 Market Core Swing Day Techniques & Tips Self (Psychology, learning style, objectives, skills, risk)

6 6 System A System B Monthly RB Overreaction 5DD Max Pain Triple screen Washout Channeling % % System B Can be a screen or set-up for System A ! System B Can be a screen or set-up for System A ! Growing the trade 2-10 days

7 7

8 8 Beliefs about Self

9 9 Beliefs about Self: “quickwrite” Identity Purpose Strengths & Weaknesses Goals & Objectives Strategy

10 10 Bias Self-attribution Knowledge illusion Illusion of control Biased 2d hand knowledge Hindsight bias Confirmation bias Illusory correlation Overconfidence, Optimism bias Illusory trends & patterns Sample size Representativeness heuristic bias Illusion of Validity Illusion of Validity

11 11 The inside of my head is a busy place Chief of Staff Staff Call System A System B1 CEO System B2 System B3 System B4 System B4 R&D Cust Svc TradingPrototypeAcctingBenchmark Production

12 12  Investing without a strategy (time, risk, amount, goals)  Individual stocks rather than a diversified portfolio  Investing in stocks rather than companies  Buy high  Sell low  Churning their investments  Act on "tips" and "sound bites"  Too much in fees and commissions  Make decisions based on tax avoidance  Unrealistic expectations  Neglect  Risk tolerance The Problems CFA Institute's Top 12 Investor Mistakes

13 13

14 14 Systems Beliefs

15 15 The Trading System & Plan Trading Systems Market filter Setup conditions Entry signal Protective Stop Re-entry strategy Exit strategy Position sizing algorithm Trading System Executive summary Business description Industry overview Competition Self Knowledge Trading Strategy Beliefs, alliances, coaching Trading edges Financial Info Contingency planning

16 16 Beliefs about Systems A 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 InputProcess Environment Output

17 17  Beat the market  Highest return within risk tolerance  Achieve required return at the lowest risk  Unit of return vs unit of risk  Longevity vs shortest time to achieve goal  Be small when wrong, large when right  Feel professional (BE PROFESSIONAL) Be careful what you ask for Objectives

18 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 19 Decision making systems

20 20 Oh! The Choices you’ll make! Risks Risk management Trading vehicles Trading systems Trading strategies Time Frames Objectives

21 21 Market Beliefs

22 22 What’s the nature of the market? Description Dynamic? Process Strategy Process Value Simple Random Chaotic Complex Complicated 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 23 Performance Math Market Sector Stock 50% 25%

24 24 Market Classification 2/31/6

25 25 Market classification strategy 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 quiet normal volatile

26 26 Market condition Bull Sideways Bear QuietNormalVolatile

27 27 Market condition Bull Sideways Bear QuietNormal 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 551w screen

28 28 Mental Models

29 29 Sector Analysis Large ValueBlendGrowth Medium Small The Morningstar Cube

30 30 Efficiency of Hierarchy Mkt DowNASS&P Companies Sectors Major Indices Equity Mkt S BG M V L "Morningstar Cube" Top-Down Approach

31 31 Mkt DowNASS&P Companies Sectors Major Indices Equity Mkt S BG M V L "Morningstar Cube" Investor Management Lens/Filter (provided by fund managers) Top-Down Approach Efficiency of Hierarchy

32 32 Liquid US Index ETFs: Can be shorted on a downtick DIASPYQQQ IJJMDYIJK IJSIJRIJT ValueBlendGrowth Large Mid Small World Market Model

33 33

34 34 Equities Beliefs Real Estate Business Stormy Weather Results Losing Streaks Experts Advertising Media Self-doubt Emotions Success Guilt

35 Statistics 35

36 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 37 Extremes

38 38 Getting on the bandwagon Innovators Early adopters Early mass adopters Late mass adopters “Grumpy old men” % 100% 50%

39 Systems 39

40 Systems and timeframes 40

41 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 zone I 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 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

43 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 44 Green zone & Yellow zone trading Any swing pattern can get us here How to think about trading the “Gap fail” tHOD Rangestat tLOD

45 ETF 2 45

46 “I got 3% return, is that any good?”

47 47  comparing a range of performance  comparing apples and oranges  "normalizes" data, helps trendspotting (x-min) (max-min) 100 * 3- (-5) 15- (-5) = 40100* 3- (-12) 8- (-12) = * Indexing

48 48 Top Down analysis  Market Condition  ETFs  Regions Top Down analysis  Market Condition  ETFs  Regions Calculations  Strength  Consistency  Quality  Asset allocation Calculations  Strength  Consistency  Quality  Asset allocation Reports  Benchmarking  ETF "stars"  Regions  ETF swing trading Reports  Benchmarking  ETF "stars"  Regions  ETF swing trading Goals  Consistency  Discipline  Routine  Simplicity Goals  Consistency  Discipline  Routine  Simplicity ETF 2.0 summary

49 49 ETF 2.0 Strength Average Quality Consistency + + = Strength: calculate RS (blended 3 & 6 month performance) STR Consistency: indexed, 10 week weighted average of Relative Strength CON Quality: indexed, 40 week “Quality rating” (Avg%Gain) / (StDev) QUAL Average: the average of STR + CON + QUAL AVG Strength: calculate RS (blended 3 & 6 month performance) STR Consistency: indexed, 10 week weighted average of Relative Strength CON Quality: indexed, 40 week “Quality rating” (Avg%Gain) / (StDev) QUAL Average: the average of STR + CON + QUAL AVG

50 50 ETF 2.0 assessment ( ) 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 51 ETF 2.0 assessment (adds ) 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%

52 Index Overreaction

53 53 Index Overreaction  Strategy :  Main indexes only  Trade only with the long term trend  Significant short term move away from the trend.  Short term trade to capture the snap back  Key Concepts :  ATR % defines significant move  200d MA = long term trend  10d MA = short term trend  Volatile move away from the short term trend  Snap back to short term trend usually "over-corrects"

54 54 Index overreaction  Profitable every year from 1994 to 2004  SPY, QQQQ, MDY, IWM, SMH  Made money in both bull and bear markets  Simple to trade and easy to learn  mechanical system  Consistent money maker on long & short side  Outperformed buy and hold  A few simple rules, 5 minutes a day or less to implement  Statistics based entry, based on volatility (dynamic)  Concept: the market corrects after a significant overreaction away from the trend

55 55 Overreaction: Buys #RuleComment 1 Today's close >200d SMATrade with dominant LT trend 2 Today's High < 10d SMAPullback from main trend 3 Today's Close 1x ATR%< 10d SMA Strong 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 56 Overreaction System Rules: Sells #RuleComment 1 Today's close <200d SMATrade with dominant LT trend 2 Today's High >10d SMAPullback 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 57 Index Overreaction System summary Comments  Don’t need to monitor all day  Takes advantage of long and short sides  Cash is not tied up  Can calmly enter the market in the currect direction in emotionally challenging markets  Mechanical signals don't require discretionary judgement  High percentage of winning trades Application  Trade a basket of ETFs  Keep it simple and emotion free: apply the rules  Paper trade until you are comfortable  Trade small position sizes

58 Index Channeling

59 59 Channeling: Buys #RuleComment 1 Today's close >200d SMATrade 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 60 Overreaction/Channelling Stops Considerations: 3% trailing stop for broad US indices 5% trailing stop for IGW + international broad indices

61 61

62 5 days down (5DD) 62

63 5DD concept 63

64 551w 64

65 “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)

66 “551w” 66 Draw a concept diagram Use the “framing” structure

67 “551w” concept diagram 67

68 “551w” concept diagram: a way 68

69 Washout 69

70 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 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 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 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

74 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 Entry Day Exit Entry

75 75 Reward: Risk Swing High Entry Exit ATR Trailing stop W%R(260) > -80 Institutional confidence

76 76 Slightly lower reliability Lower average R win, SQN More opportunities per week Still tight risk controlled

77 Triple Screen System variation on Dr Alexander Elder's system

78 78 Triple Screen Overview  Trading For A Living, 1993 by Dr Alexander Elder  combines 3 time frames to remove disadvantages of each  combines the use of trend-following and oscillating indicators  Each time frame relates to the next by a factor of 5 (per Elder)  You can round off the time periods  Example: if the middle time period is daily, the short term period can be hourly, not 1hr and 12 min (6 market hours divided by 5)  Screen 1 uses the longest time frame, Screen 3 the shortest Screen 1Screen 2Screen 3 Market MovementLong termIntermediateShort term Type trendStrong trendCounter trendBreakout Timeframe example Monthly Weekly Daily Hourly Indicator example ADX > (25) or MACD Hist uptick 20 dMA or Williams %R Candlestick breakout

79 79 Screen 1: Major Movement Screen 2: Intermediate Movement Screen 3: Timing  Find strong trends  Apply an oscillator to daily chart  Use daily declines suring weekly uptrends to find buying opportunities  Use daily rallies during weekly downtrends to find shorting opportunities Triple Screen Concept

80 80 Weekly trendDaily TrendActionOrder Up WaitNone UpDownGo longTrailing buy stop Down WaitNone DownUpGo shortTrailing sell stop Screen 1: Major Movement Screen 2: Intermediate Movement Screen 3: Timing Triple Screen Strategy Summary

81 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 82 ADX > 25, +DI > -DI or MACD-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 83 QQQQ

84 84

85 85

86 86 Daily ETF “Triple Screen” screen

87 Mastermind Insights 87

88 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

89 Trade Index Analysis

90 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 91 Trade Index Analysis 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: 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: 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 Gain

92 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 Gain Best Possible Exit #2

93 93 Trade Index Analysis 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! 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! Market A complex adaptive system Market A complex adaptive system stalking trade entry exit ruleset

94 94 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 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 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 Trade Index 1R Time period (t)

95 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 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 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 Trade Index 1R Time period (t) Opportunity!?

96 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 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 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 Time period (t) DD 44

97 97 Applying Exit Efficiency

98 98 Technique

99 99

100 100 5 day Slope of the 50d MA 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

101 101 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”

102 102 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 The Universal Entry

103 103 Risk Management

104 104  Diversification  Position sizing  Portfolio heat  Benchmarking  System trading  Objectives  Risk tolerance  Expectancy  MA of equity  20 trade MA of expectancy  Fundamentals  Extreme value  Assume you are wrong until the mkt proves you right  Debriefing  Trading plan  Business plan  After action reviews  System of systems Risk management

105 105 Market Assessment

106 106 How much of the portfolio? Set-up Stalking Entry Initial exit Capital preservation Profit preservation Exit Profit target? $/share Reward Risk How do you decide? Position Sizing

107 Exercises 107

108 108 How do you feel about these charts? Like/dislike? Long vs Short vs Stand Aside? What will it do next?

109 109 1

110 110 2

111 111 3

112 112 4

113

114 114

115 115 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)

116 Range Stat

117 AA case study example of rangestat

118

119 AA intraday range stats +4% +2% +6% -4% -2% -6% close open Yesterday’s candle close Intraday moves Max12.36% +1SD5.28% Avg3.50% -1SD1.71% Min1.20% StDev1.79%

120 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

121 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

122 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

123 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 Hypothetical trade frame

124 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

125 125 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

126 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

127 127 Research Program

128 128 Multivariate world market correlation model Underlying causal model “competitive themes” “hidden dynamic order” Geographic US Japan Europe Asia EAFE (not US) Latin Am Emerging Mkt Business sector US sectors (SPDR list) Global sectors (list) Style Value Blend Growth Independent Market Cap Large Medium Small Micro Asset class Equities Real estate Bond/income Commodities Currency USD Euro Yen 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?

129 129 Forecasting model committee 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? Statistics Multivariate Principle Components Ebbs and Flows Dynamic Model base Tortoise 2.0 Short term RS & volatility 8-10 winners Sector, region limits Business forecast Internal model base Data pattern driven Algorithm selection Competition winner Monte Carlo 10 year, monthly % Mean reversion Performance Volatility Rules based Hybrid, short term Linear regression Market condition Regional focus Neural Network Monthly prediction Weekly prediction “Black Box” Expert architecture CART Classification Regression Tree Non-linear Explanatory power Momentum Fama 12 month rules ST momentum IT momentum LT momentum Annual Rebalance 10 sectors January rebalance No timing Long only Buy & Hold Total Market Index Baseline Model Predictions Historical Performance Analysis Assessment Strategy Selection Performance Assessment Strategy Assessment Lessons Learned %return & %variation Of Models & System %return & %variation Of Models & System Model forecasts Model preferences Model forecasts Model preferences Price based Model-specific time frame Price based Model-specific time frame Rules for combining Rules for weighting Rules for combining Rules for weighting Compare & contrast Agreement, disagreement Compare & contrast Agreement, disagreement Rules & decisions Model performance Rules & decisions Model performance Evaluate System rules Apply learning Evaluate System rules Apply learning Each decision cycle

130 130 World Market Model: Directed Acyclic Graph (DAG) Diagram %return %variation Region Geographic US Japan Europe Asia EAFE (not US) Latin Am Emerging Mkt Business sector US sectors (SPDR list) Global sectors (list) Style Value Blend Growth Independent Market Cap Large Medium Small Micro Asset class Equities Real estate Bond/income Commodities Currency USD Euro Yen Themes & dimensions Currency Global sector Style Mkt Cap Asset Class US Sector

131 131 ETF components VTI Total Mkt Index VTI Total Mkt Index US Business sector Global Business sector Asset classes Regions Currencies Style Capitalization

132 Live Trading Stats

133 Live Feb 2011, day 1 133

134 Live Feb 2011, day 2 134

135 Live Feb 2011, day 3 135

136 Live Feb 2011, day 4 136

137 Live Feb 2011, day 5 137

138 Live Trading Prep

139 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 zone I 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

140 Daily Trading Plan Notes (a way) 140

141 Daily: Plan-Prepare-Execute 141

142 142 Max(ever) Min(ever) Min(future) Max(future) Max(x) Min(x) Avg(x) Avg+1SD(x) Avg-1SD(x) 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

143 143 SPY EFA QQQQ MDY XLE EWZ ILF EPP MVVMZZ UWM QLD IWM QID TWM EEM EEV FXP FXI EFU IEV XLB XLF XLI SKF SMN BAC AXP JPM VOT HPQ CSCO MSFT AAPL CLF GLD AA SLV AGQSLW ZSL GDXGDXJ CVX CAT HD EWM XME NFLX DBA DBC WMT TLT DVN

144 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

145 The Curve

146 146 Consider the curve What do you see? What questions do you ask?

147 147 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?

148 148 Fair value On Average: Where are you buying? Where are you selling?

149 149 Slope? Time period? Normal? Trend? Duration? Frequency & amplitude?

150 150 Fair value Slope? Variation? Stretch? Normal? Boundary of normal?

151 151 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? Sideways? Bull? Boundary conditions?

152 152 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? Sideways? Bull? 5DD 5DDC 5DD 5DDC 5DD 5DDC ETF2 WO WOC WO WOC ETF2 ETF O ETF CETF O Triple Screen Triple Screen Triple Screen

153 153 Attitude Checks

154 154  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

155 155 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

156 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?

157 157 Stalking Not predicting Knowing your prey Identifying the patterns Knowing the odds Setting the conditions Taking the shot

158 158 Bears go fishing

159 159 Lions await the herd

160 160 “YOU DON’T KNOW NOTHING”

161 161 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.

162 162 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

163 Technical Analysis 163

164 164 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

165 165 Extremes

166 Technical Analysis Review Average Directional Index (ADX) Average True Range (ATR) Moving Average Convergence/Divergence (MACD) Williams %R “NDX” (an improved Williams %R) Candlestick Charting 200day MA “Stretch” % Slope of the 30d regression line Gap Stat Range Stat

167 167 Getting on the bandwagon Innovators Early adopters Early mass adopters Late mass adopters “Grumpy old men” % 100% 50%

168 168 Average Directional Index (ADX) (strength of trend)  Invented by Welles Wilder  measures strength of trend  simple 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.

169 169 ADX (continued)  Normal calculation: 14 day period with end of day data  ADX >30 indicates there is a strong trend  Momentum 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

170 170 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

171 171 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 index DX = current DX

172 172 ADX calculation DX = +DI14 minus -DI14 +DI14 plus -DI14 x 100 DI difference DI sum x 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

173 173 Trendspotting with ADX

174 174 Average True Range (ATR) (measuring volatility)  Average True Range ("ATR") is a measure of volatility.  Introduced by Wilder in New Concepts in Technical Trading Systems  Common 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

175 175 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 day inside day A B C A A C C B B

176 176 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.

177 177 The 4 seasons of MACD-Histogram

178 178 Williams %R (a measure of overbought/oversold)  Commonly performed on a 10 day period  Scale: 0 to minus 100 (can ignore the minus sign)  0 to 20 considered overbought  80 to 100 considered oversold  Must wait for price confirmation: a better setup than trigger  Uncanny in its ability to anticipate turning points  Formula: Highest High(n) - Close Highest High(n)- Lowest Low (n) x 100

179 179 Williams%R in action

180 NDX vs Williams %R Williams %R 10 NDX  uses current day data and previous 9  readings are not intuitive  uses previous 10 days of data  readings are intuitive  extreme moves today are highlighted

181 181 Candlesticks Quicklook  Visually display much more info than bar charts  Quicker to identify important patterns than bars  Should be used in conjunction with Western technicals  Should not be used on their own for entries or stand alone systems  Do not give price targets  Reveal market psychology  Tug of war between bulls and bears  Can signal change of trend or market pauses  "Windows" or "gaps" are very powerful signals  Long shadows can identify support or resistance when taken in combination  Work in multiple time frames  Generally well suited for intermediate and short term timeperiods  Pay attention to Doji

182 182 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)

183 183 Candlestick examples 3 soldiers marchingLong shadows (support) Triple cloud cover Hammer Gravestone Doji: indecision Engulfing Evening star

184 184 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?

185 200dMA % slope 185

186 186

187 200dMA stretch%: All indices 187

188 30day Regression line slope 188

189 189

190 Gap Stat 190

191 Range Stat 191

192 192


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