Copyright (c) LBRGroup All Rights Reserved.

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Description: Copyright (c) LBRGroup All Rights Reserved.

Copyright (c) LBRGroup 2011. All Rights Reserved.
Market Structure and Trade Management Strategies Fundraising for Christchurch NZ and Japan June 14, Linda Raschke LBRGroup.com Copyright (c) LBRGroup All Rights Reserved.

Copyright (c) LBRGroup 2011. All Rights Reserved.
There is a free 133 page manual you can download that elaborates much more on structure, swing trading, execution and market psychology at: FuturePathTrading.com This power point presentation will be posted to lbrgroup.com for you to download on Wednesday. Copyright (c) LBRGroup All Rights Reserved.

Filtered Waves – Basic Theory Arthur Merrill, 1977
Arthur Merrill’s filtered waves used a percentage of price movement (5%) to filter out the noise. His work was important because he dedicated a whole book to the concept of classifying patterns by the wave structure. Gann was probably the most popular predecessor of swing charting. Welles Wilder documented the use of ATR (Average True Range) functions to quantify swings as opposed to percent functions. This was applied to a system called the Volatility stop and reverse system. Merrill’s work was far more comprehensive, in that not only did he classify previous market structures, he also developed classic actuarial tables, or probability grids, to measure the median % increase/decrease and duration of bull and bear markets. Copyright (c) LBRGroup All Rights Reserved.

Market Structure as measured by Swings
Waves eliminate “noise” and thus make is easier to determine TREND Additionally, the absolute support and resistance are one more way of adding structure. While Art Merrill used a percent function, we use an ATR function. A new Up Wave starts when price moves 3 ATRs up from the lowest low or lowest close over 15-period look-back. A new Down wave starts when price moves 3 ATR’s down from the highest high or highest close over 15-period look back period. Once market swings have been quantified, relationships, turning points, and trend can once again be classified, counted, examined and expressed in numbers. We can start to build a probability grid, evaluating trade management strategies. Copyright (c) LBRGroup All Rights Reserved.

Market Waves Define Structure
Copyright (c) LBRGroup All Rights Reserved.

Copyright (c) LBRGroup 1996-2006. All Rights Reserved.
SP index – waves define structure and trade opportunity. Some wave patterns lead to small wins, some have above average odds of a bigger win. Copyright (c) LBRGroup All Rights Reserved.

BNI – Wave structure is one of the primary basis of a trade.

Gold – Wave structure is one of the primary basis of a trade.

Trend Reversal in Sugar

Trend Reversal from up to down again – Weekly chart

Wave Structure – Dollar in an up trend

Waves structure still shows uptrend

Uptrend still intact, volatility contracting

Trend Reversal Down when “Power Buy” fails

Up waves are now shorting opportunities

The first trend reversal up

A failed bear flag is the same as a failure test or failure swing. The breakout from this equilibrium point did not lead to a big win. A trailing stop may have still led to a small win. Copyright (c) LBRGroup All Rights Reserved.

How can structure aid in identifying the sweet spots in the data?
Trailing stops more often then not degraded most systems built from relatively simple rules. There are no combination targets and stops, even with the use of one filter, where it is more profitable to trail a stop then playing for a small win. However, using two time frames, can help identify “sweet spots’” in the data that do lead to bigger wins or a higher then average expectation of greater trendiness. Copyright (c) LBRGroup All Rights Reserved.

Trailing Stop Functions
ONLY should be used at specific structural points! Breakouts, Trend reversals as defined by the wave structure, and after A-B-C formations in a trending market Copyright (c) LBRGroup All Rights Reserved.

“Sweet spot” with detail on short time frame

Japan Index – Trend reversal is a Process!

DJ Utilities Index – Weekly Chart

REMEMBER – “textbook” trades occur a small percentage of the time ONLY. This is a game based on probabilities where overtime you will have a small edge and hopefully you will “get lucky” once in a while. Copyright (c) LBRGroup All Rights Reserved.

Dollar Index – Daily Chart

Dollar Index – Hourly Chart

Goldman Sachs Spot Index - Weekly

GSI – Daily chart with more data

Copper Intraday – detail of failed A B C

4 Basic Patterns: Simple continuation pattern (bull or bear flag in a trend): Enter with limit order or market order, play for small win (.5 to 1.5 ATR), Initial Risk 2 ATR. Tighten stop when market moves in your favor Corrective A-B-C: enter after you see your stop point, Play for big win (more then 1 ATR) or new highs or lows. Initial Risk: 1 ATR from swing high or low Failed A-B-C (trend reversal): Enter on buy or sell stop, play for big win or trail stop. Initial Risk: 1 ATR Breakout from converging trendlines. Enter with buy or sell stop or market order, add on first consolidation or pause. Trail stop or play for big win. Initial Risk: 1ATR from first entry. Copyright (c) LBRGroup All Rights Reserved.

First Flag after breakout CAN play for Big Target.

Bear flags late in the swing: small target ONLY.

“Failed” A-B-C or trend reversal point. Trail a stop.

Breakout from converging trendlines (wedge): Trail Stop

Initial Risk at Keltner Channel, Trail stop once Parabolic Kicks in

DAILY CRUDE – VOLATILITY STOP

Testing for the Edge Randomly entering a position can produce a profit 65% of the time. By applying simple filters, random entry can become breakeven or even slightly profitable. This data gives a benchmark for a systematic trader to beat in order ensure the system is successful due to more then capitalizing off noise. “Noise” is analogous with random price data. Let’s go through a 5 step exercise using a randomizer to generate trade entries as a departure point for modeling price behavior. Copyright (c) LBRGroup All Rights Reserved.

Relationship between targets & stops with random entries
Stop and reverse off randomly generated trade points yields a 50% win/loss ratio as well as a profitable system 50% of the time. Random entry with Large Target and Large Stop (3 ATRs): The win/loss ratio still hovers around 50%. (3 ATRs is just outside the “noise’ inherent in price action). Random entry with Small Target and Large Stop (1ATR/3ATR). The win/loss ratio jumps up to 70%+. While the chances of getting 1 ATR before a 3 ATR stop is hit are greater then 70%, it still is not a profitable system. If we use a target of .5 ATRs and leave the large stop in place, the win % jumps to 82%. The increase in % win should be encouraging for the short term discretionary trader. Random entry with Small Target and Time Stop (1ATR/8bars). Though the % win drops to 65%, the size of the average loss decreases. So, while a larger fixed stop gives a trade more time to be profitable, a discretionary trader may choose to exit if the trade does not work within a reasonable amount of time. Copyright (c) LBRGroup All Rights Reserved.

A profitable system that is durable and robust based off random entries! At this point we know we can randomly enter a trade and get better then an 80% win rate on most markets. By adding a trend filter, random entries can be made into a profitable system. Only long entries are taken in an up trend and only short entries are taken in a downtrend. (Again, all entries are at randomly generated points). The Profit Target is 4 ATRs and the Stop Level is set to 3 ATRs (just outside of the “noise”). This ensures that if we can get better then a 50% win rate, the system will be profitable. This system was run 100 times on each market with randomly generated trade entries each time. The time period tested was from 1995 to 2005 on 22 domestic futures markets that included currencies, bonds, index futures, metals, agricultural products, and softs. Copyright (c) LBRGroup All Rights Reserved.

Statistical profile of random system with one Trend Filter:
The system was profitable between 92 – 100% of the time on all markets with the exception of live cows and hogs, (which tend not to trend for extended periods of time), as well as silver and natural gas – (probably due to its extreme spiky nature in recent years). All index futures were profitable 100% of the time over 100 randomly generated runs over 10 years of daily data! This is not a recommendation to trade this way, but RANDOMLY generated entries can lead to profitable strategies with use of one filter if stops are placed just beyond the noise and a larger target is played for. Copyright (c) LBRGroup All Rights Reserved.

Summary: Random entry produces a high win/loss rate when the stop is large enough to be just outside the “noise”. By using a time stop, we can avoid taking repeated large losses at the expense of a slightly lower win rate. With addition of one trend filter, a larger target can be played for. These raw statistics provide a hurdle that a system must exceed in order to produce better then random results. A system can be considered better then random entry when: The drawdowns are smaller, or The P&L is substantially larger then random entry, or The win rate is significantly higher then that of random entry. The first step to improving a system is by the addition of some type of trend filter. The greatest edge us trading in the direction of the trend. Copyright (c) LBRGroup All Rights Reserved.

Relationship between Entries, Stops, and Profits
There is not always a relationship between a trade is entered and where a trade is stopped out. For example, with time based stops, the stop out level has no relationship to the initial entry price. It’s not necessarily the entry that makes a model or system successful. It’s all about the exit strategy! The larger the target, the lower the % win rate. The smaller the target, the fewer times the stop will be hit (all else being equal). The wider the stop, the higher the % win rate. Copyright (c) LBRGroup All Rights Reserved.

Momentum Driven Models
While Volatility and Channel breakout systems are purely price driven, momentum functions are based on a derivative of price. Momentum has many interesting properties, including a leading function for price. The tradeoff is an increase in “noise” over smoothed functions such as moving averages, which have a lag. What is the best combination of exit strategies, stops and targets to compensate for this increase in noise? A variety of look back periods for momentum functions were examined and tested against permutations of fixed targets ranging from .5 ATRs to 3 ATRs. Fixed stops, time stops, as well as delayed entries (so as not to be entering on a “momentum spike” were also tested. Similar conclusions could be drawn as what was modeled with random entry systems: Smaller targets are more profitable then Large targets, Time stops are better then fixed stops, and Time stops combined with Fixed stops are optimal. Momentum driven models perform better with a weighting given to additional use of time stops which was not the case with random generated entries. Copyright (c) LBRGroup All Rights Reserved.

The End! Thanks for Attending.

ATR (Average True Range) Stops
(a most important concept!) ATR = Average True Range “True Range” includes “gap” area + Day’s range ATR is a moving average of the past N- days “True Range”. Copyright (c) LBRGroup All Rights Reserved.

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