X-Raying the Black Box - afta-dfw.org Dickover X-Raying the Black Box - Feb... · X-Raying the Black Box By Mel Dickover AfTA, Dallas, Texas Feb 17, 2015

X-Raying the Black Box

By Mel Dickover

AfTA, Dallas, Texas

Feb 17, 2015

Conventional Technical Analysis

• Analyze price chart, down to the minutiae of bars

– Use indicators to transform price and volume

– Prediction through patterns • Indicator patterns are predictive

• Price Pattern view: multi-bar patterns are predictive

• Candle pattern view: even a single bar is predictive

• Price Action view: price bars only, ignore volume

– Prediction through broader Meta-patterns • Hypotheses describe inherent market movement

• Fibonacci, Gann, Elliot Wave

• What is visible on the chart is all, nothing is inside

• The cause of price movement is a mystery

2/17/2015 Copyright February 2015 by Mel Dickover 2

Epiphany

From

XKCD.com

2/17/2015 3 Copyright February 2015 by Mel Dickover

M²M² (Mel’s Model of Market Movement)

• An empirical model of market movement

• Price movement fully explained by

– News driven price impulses

– Cycles

• Empirical evidence from Fourier Analysis Spectrograms

– Support and resistance constraints

• Empirical evidence published in my April 2014 TASC article titled “Evidence-Based Support and Resistance”


Fourier Analysis for trading

Relation to Nirvana’s

Spectrum indicator

In the ACT module

We can focus on and extract any

cycle by identifying its period on

the spectrogram

Adding up all the cycles

gives us the “signal” we

call a price chart

5 Copyright February 2015 by Mel Dickover

A Fourier Analysis animation

2/17/2015

Stock Spectra (Daily Bars)

Copyright February 2015 by Mel Dickover 6 2/17/2015

ES Spectra (5 min bars)


Two weeks 9 weeks

21 weeks

Cycles are relatively stable

over long periods, across

sessions with gaps

FX Spectra (720 min bars)


Stability of cycles

• Cycles tend to be long lasting, with stable wavelength

– In all the liquid markets I have looked at

– Stocks, ETFs, Futures, FX

• What varies the most is the amplitude

– Changes in relative amplitude causes some cycles to disappear and reappear on the plot, due to the plotting threshold of the heatmap

• Phase does not seem to vary much except as a result of news events


Isolate an individual cycle’s contribution

• From known wavelength, either

– Use zero mean roofing filter to extract detrended price movement of that cycle

– Multiply phase from Bandpass Filter tuned to that cycle by amplitude calculated in various ways

• Result is the detrended contribution to price of the cycle at the selected wavelength


Passband selects

wavelength range

DeCycler filters wavelengths

longer than cutoff

Super Smoother Filter removes

wavelengths shorter than period

Ehlers Roofing Filter


•Spectrogram found cycles of

36, 46, 75, 90, and 135

•Roofing filter created

detrended price movements

for 36, 46, 75, and 90

wavelengths

•Added them up with a 135

period DeCycler MA to get the

blue line

•White line is a 10 period

Super Smoother Filter

Add up cycles to reproduce price movement

First Approximation

All price movement accounted

for by cycles and price

impulses from news

Where do price patterns come from?

Take a long cycle, add a intermediate cycle, get head and shoulders, double

tops, channels. Add a third short cycle, consolidations, flags, triangles,

pennants, etc.

All price patterns that we see in a chosen time frame come from adding up

three or four cycles of wavelengths of 10 to 140 in that time frame.

Because this recurs in any time frame we choose, we call the market fractal

12 Copyright February 2015 by Mel Dickover 2/17/2015

The Pipe Organ as market metaphor

• The background music of the market (bass section)

• Some cycles are standing waves, driven by ongoing smooth forces

– Business cycle, economic sector rotation, and seasonality

• Wavelength stays the same over the years, amplitudes may vary somewhat.


The Background Cycles


• The Leontief Input/Output Model describes the relationships among

the components of the economy

• Interactions among the components generate the cycles, due to

expectations, and probably vice versa

•Market sectors rotate months ahead of the economic sectors due to

the Wyckoff Accumulation/Distribution cycle (cause of long trends)

Business Cycle Sector Rotation Cycle

The Harp as market metaphor

• The foreground tradable melody of the market

• Cycles form when a string in the market is plucked by a news event (price impulse)

• Periodic plucking can cause standing waves (e.g., taxes)

– If a wave is not reinforced, it will eventually die out


What explains foreground price cycles?

• All price changes are the result of changes in the balance of supply and demand

• All changes in the balance of supply and demand are the result of changes in the expectations of what the price will be in the future

• To explain cycles, we need to explain why expectations cycle

• Two features of cognitive psychology are sufficient to explain the cycling of expectations

– Anchoring

– Confirmation bias


How the Psychology Works


Expectations,

Anchoring, and

Confirmation Bias

Sufficient to

explain the cycling

response of the

Market

Emotions are a

result, not a cause

News event driven cycles • Traders process a price impulse, generate a price expectation,

in light of the current trend (that is expected to continue, due to anchoring)

• Traders start to think price has gone far enough, begin taking profits, and cause price to reverse. Price is now going countertrend, traders expect it to reverse, and the traders’ response has the cycle off and running

• Traders expect cyclic price behavior, use oscillators and Bollinger Bands, believe in two standard deviation reversals

• In any time frame, there are aggressive, conservative, and follower traders – They generate a consensus on amplitude

• Defended Prices constrain the amplitude of forming cycles


Supply and Demand Pools

• Defended Price Lines are empirically discovered prices where pools of supply and demand are

– Supply and demand are unevenly distributed across price

– Significantly higher volume rushes in to defend price • Detected by Relative Volume indicator

– Price movement is suddenly restricted • Detected by the Freedom of Movement indicator

• FoM is a normalized measure of relative effect (relative price change) divided by relative effort (relative volume)

• If price movement is unrestricted, it only takes small to moderate volume to move price (e.g. trending)


Freedom of Movement™, Relative Volume


Defended Price Lines locate real S&R


DPLs persist for years on daily chart


ES DPLs (detail)


ES DPLs (Longer term)


Defended Price Lines™ • Price tends to pause at , pullback from, consolidate at, or

reverse at , or gap to DPLs – Since the pool must be absorbed for price to move past it

– DPLs persist for years on daily charts, for weeks or months on 5 minute charts

• Being empirical, rather than hypothetical, they replace Fibonacci lines, price extremes, opening range lines, floor pivots and other hypothetical price formulas

• Uses – Allow meaningful calculation of reward:risk

– Show you where to place option strikes

– Show you where price will jump to when gapping

– Prices to set stops


Real versus Apparent Support and Resistance


Yellow trend lines are apparent

S/R, an artifact of adding cycles

•Real S/R occurs at Defended Price Lines

•Cycle turns and DPLs may not coincide

White line is the sum of the

green and orange cycles,

plus a trend

Why it’s so hard to see inside clearly

• Technology issues

– Not all the cycles are relevant to your trading and the

relevant cycles are embedded in other cycles

– Indicators may not be tuned to the relevant cycles

– Noise

– Unwanted influence of longer wavelength cycles in your price data

– Lag

• Conceptual issues

– Not making the essential market mode distinction

– Real support and resistance is not apparent to you


Seeing clearly means seeing individual cycles

Copyright February 2015 by Mel Dickover 28

Price chart

Individual cycles

seen clearly

Fourier Analysis of

the spectrum

2/17/2015

Choosing a time frame is choosing some cycles

• Choosing a time frame makes two cycles very important – The Trading cycle and the Trend cycles are the ones with the strongest

contribution to price movement in their wavelength range

– Trading cycle in the chosen time frame (~ 10-48 bars)

– Trend cycle in the chosen time frame (~ 80-140 bars) • Explains why trends persist, and allows you to calculate how long they will persist

• The “trend” is the slope of the trend cycle

• Trend and trading cycle are relative to time frame – Time frames are like nested Russian dolls

– The one outside is the trend, the one just inside is the trading cycle


A time frame

CAT Spectrogram – Trading cycle


Trading cycle of 46-49 periods has

been stable for years

2/17/2015

CAT Spectrogram – Trend cycle


Cycles have been the same wavelengths for years,

but vary in strength relative to each other

2/17/2015

Market Mode is a relation between cycles


Small

Momentum Big

Momentum

Amplitude

Slope when

trending

Slope when cycling

Relationship: amplitude/ momentum

Cycling: amplitude is larger, swings are tradable both ways

Trending: momentum is larger, only trade pullbacks or trend follow

• Noise defined: (High-Low) on a bar.

– From systemic reasons and from digital sampling (aliasing)

– Aliasing noise is seen in periods below 10 bars

– Ehlers Super Smoother Filter removes sampling noise and plots the closest thing to a “true price” that I know

• If noise is too large compared to the amplitude of the trading cycle, we have “chop” and the market is untradeable

– Ehlers Signal to Noise Ratio indicator can measure this

• Use of price extremes (High or Low) in indicators is using noise to trade

Noise makes the price unclear


Seeing cycles clearly with oscillators

• The purpose of an oscillator is to faithfully track the phase of one particular cyclic component of price

• Issues in doing it well: – Like not tuning an analog radio to the correct frequency, not tuning an

oscillator to the frequency of the relevant cycle gives fuzzy or false signals

– Using price data that includes sampling noise plots a jagged oscillator that gives whipsaw signals

– Price data that includes the influence of the longer wavelength cycles, oscillators get pinned by strong trends, losing track of phase

• Ehlers has technology that addresses all three problems in oscillators


Use Ehlers technology

• Adapt an indicator to the trading cycle – Automatically, computing the period in the indicator code

– Manually, using the spectrogram

• Compute oscillators based on output of Ehlers Roofing Filter, not raw price data – The DeCycler removes the influence of longer term cycles

– The Super Smoother Filter removes higher frequency components of price (aliasing noise) from wavelengths shorter than its period

– The Roofing Filter is computed by using the output of a longer cutoff DeCycler and smoothing it with a shorter period Super Smoother Filter


RSI oscillator comparison


Passband selects only 46 bar wavelength

Passband allows 12 to 50 bar wavelengths

Spectrum contains 18, 27,36,46, 75, 90, 132 bar cycles

Why oscillators go against the price • What wavelength data is in the passband?

– One cycle only: in the price chart, that contribution is added to longer trend cycles that might overwhelm it

• Trending, not cycling: longer wavelength is dominating

– Multiple cycles: allowing other cycles plots wiggles in a direction different than the cycle you are trading

– A superposition of cycles is complicated to disentangle • Having a trading cycle does not mean that the other cycles can be

safely ignored

• Encountering a Defended Price Line

– Regardless of what the cycles are trying to do, support and resistance cannot be ignored


Lag Comparison of Moving Averages


Reducing lag

• Lag matters when price moves quickly or gaps

– If price isn’t moving, filters are all the same

• Building your indicators on a lagging infrastructure results in more lag than you need accept

– Making good filters is an DSP engineering tradeoff between noise, smoothing, lag, and frequency response

• So far, Ehlers does this tradeoff the best

– Anywhere moving averages are used, replace them with • Ehlers Super Smoother Filter or the Ehlers DeCycler, depending on

what frequency response is desired

– Except where the statistics requires a true average


Predicting Price with Chart Pattern Analysis

• The most basic form of technical analysis

– Identify formations on bar or candlestick charts

– Usually trend lines are drawn to identify patterns

– Sometimes volume is used with the pattern

• Patterns have statistical properties

– Success and failure rates

– Probability of which direction price will go

– Estimates of size of move expected

• Pattern predictions are like dice throws

– Know the probability of an eight, but don’t know why eight did or did not come up on a particular throw


M²M² interpretation of Price Patterns

• Conventional chart analysis looks for distinct bar formations identified with trend lines

– Using their shape to infer the direction price intends to go

– If prediction fails, it is bad luck

• M²M² argues that all price patterns arise from the superposition of the internal cycles of the chart

– If you see clearly inside the chart, the role of luck is dramatically reduced

– Much of our bad luck is due to using archaic technology


Why patterns fail

• False pattern recognition due to price noise

• Patterns may look the same, but actually be different and go in different directions (e.g., consolidations)

– Many ways to add cycles up to look similar externally

– Traders depend on the externally derived probability

– Look inside to see the intended direction for these particular cycles on this particular chart

• The uneven distribution of supply and demand

– Even though a pattern says price should go up, if there is a pool of supply to get through, that supply needs to be absorbed before price can go up. Price may consolidate or reverse, rather that go in the expected direction.


Removing price noise makes patterns clearer


Supply and demand and noise causing failure


Exploded view

To see patterns clearly

• Find patterns with a clear price line

– Ignore bar noise • Maybe most or all H-L movement is noise, maybe not. You can’t

tell until later, if at all

– Cycles are lines, so the sum of cycles is a line

– Recognize patterns based on the 10 period SSF line • A smooth “true price” at cost of about 1 to 1.5 bars lag

• Best approximates the value of the sum of cycles

• Interpret patterns in the context of Defended Prices

– Else your Reward:Risk calculation is misleading

– Apparent S/R of cyclic humps is only part of the story


The Trouble with Meta-Patterns

• Fibonacci, Gann, Golden Compass, Murray Math, Elliot Wave all purport to describe the natural way the market moves – They must be there, and you look until you find them

– Price patterns are there or not, if you don’t see them, ok

– Meta-patterns are data mined, not derived from logical models of the market, rationale is post hoc

– They embody fixed numbers (Fibs, octaves, special angles) or very complex rule sets (EW) that impose conditions on underlying cycles

– Fixed numbers can’t be right, since that implies fixed multi-cycle relationships among wavelength, phase and amplitude across symbols that are not observed in spectrograms

– Meta-patterns ignore Defended Price Lines – all they are aware of is apparent S/R from price humps

• From the point of view of M²M², the rationale and numbers of meta-patterns, except EW, have no validity


What Meta-Patterns Do

• The meta-pattern process

– Draw trend or zig-zag lines on the bars of the chart

– Match up distances with the Fibonacci numbers, Gann angles, or EW rules, until pattern is identified

• M²M² interpretation

– Trying to find the humps in the cyclic composite using noise bars with price extremes

– Meta-patterns imply some fixed relationships among the properties of the internal cycles, e.g.

• Fibonacci numbers describe the distances between cyclic humps that should exist

• Gann angles prescribe fixed relationships – even though on dynamically scaled charts, absolute angles are meaningless


Meta-Patterns in Practice

• Hindsight allows us to go back though charts and correctly fit meta-patterns to charts.

– This creates a belief in validity

– Belief in meta-pattern validity and the resulting confirmation bias causes the trader

• to ignore conflicting evidence

• Fit the meta-pattern to the chart, using the zone of noise the bars provide to fudge the fit

– If trader was lucky, a real set of humps indicating a reversal is found, if not, a false signal is found

• The problem is at the hard right edge of the chart


Fudge zone demo


First Principle

• The first principle is that

– you must not fool yourself

– and you are the easiest person to fool.

– Richard P. Feynman


Higher time frames needed for trend?

• The trend is really the slope of a longer period cycle in the current time frame

– A HTF chart merely summarizes and lags the same data you have in the current time frame

– Longer cycles are best seen in the current time frame chart using low lag filters and indicators

• Each symbol has its own answer to which longer period cycle is strong enough to be relevant

– Period is determined by looking at the spectrogram

– Not arbitrarily by going to a weekly chart


Higher Time Frame charts no more

A 150 bar DeCycler on a daily chart turns

at the same place as a 30 bar DeCycler

on a weekly chart.

Simpler and more accurate than

Current+1

Less lag = better trading

52 Copyright February 2015 by Mel Dickover

DeCycler is your instant trend line

2/17/2015

Cyclic interpretations of common setups

• Divergence results from using an MACD tuned near the trading cycle to detect reversals of the longer trend cycle – Decreasing momentum humps tell us something will happen sometime

– Tuning the oscillator to the trend cycle tells us what will happen when

• Volatility is the summed P2P amplitude of the strongest cycles in the period you are viewing, plus noise extremes

• A squeeze is a period of low volatility caused either by – cycles adding up to cancel movement for a while

– price running into a pool of supply and demand and having to work through it

• A Breakout is what happens after supply or demand is absorbed at a DPL. A Reversal is when it is not. A Consolidation or a Pullback is an ongoing attempt to absorb it.


Why Reversion-to-Mean works

• RTM generally holds a position for 2-4 bars

• Trades cycles shorter than 10 bars, which is not practical with cycle trading and oscillators

• It works by calculating the stretch away from the “real” price using an indicator like z-score or Bollinger bands

• It succeeds very well because that allows it to catch the noise price extremes

– Fades those, assuming that price will get taken back to the mean by other noise or by the short period cycle

– Profitable for both trader and broker


A clear view


Rethinking Confirmation

• Confirmation is requiring additional signals from other systems at same time, or close

– The trouble : more complex strategies, , more degrees of freedom make back testing time consuming and difficult

• Perceived need for confirmation arises from anxiety

– Unclear signals (jagged, not properly tuned)

– Ambiguous, untrustworthy pattern recognition

– Lack of trust in data mined correlations (no model)

• Clear signals are now within your grasp

– Take direction from one clear sighted person, or a consensus of the nearsighted?


Limitations of M²M²

• When or why relative amplitude of important cycles will change is not yet known

• How strong a Defended Price Line is, that is, how many shares of supply or demand await is not yet known

• What the significance of the OHLC of the current bar is, how much is truly noise, and how much reflects the current reality of the market is not known. Noise is assumed.

• The best predictions of M²M² are probabilistic, even though you know the underlying causes. Deep inside the white box is a much smaller black box.


Conclusions • Simple explanations for nearly all market phenomena of

interest result from modeling the market as cycles – Supply and demand pools are crucial in understanding why what is

happening is happening, and what is likely to happen

– Cycles and DPLs are the only predictive indicators

– Both tell you what's likely to happen, when and where, rather than adapt you to the recent past (possibly anti-predictive due to RTM)

• Patterns are smooth cycles added up – Best recognized using smoothed price

– Can only be accurately understood by looking inside the chart – external appearance can be misleading

• It is possible to develop strategies based solely on model based edges that inhere in the nature of the market – Edges that you understand and that will not go away


The elements of trading

• Fire Air, Earth, and Water view

– Chart price and volume is all you are aware of

– There is nothing beyond what can you see

– Causes are mysteries

• Periodic Table of the Market view

– Elements are wavelength, phase, amplitude, noise, lag, and Defended Price Lines

– Great reduction in mysteries


Remember

With great power comes

great current squared times resistance

–Ohm

• via Randall Munroe of XKCD

2/17/2015 60 Copyright February 2015 by Mel Dickover

References

• Technical Analysis References • “Cycle Analytics for Traders” by John Ehlers

• “The Profit Magic of Stock transaction Timing” by J. M. Hurst

• “Evidence Based Support and Resistance” (TASC April 2014) – by Melvin Dickover

• Fourier Analysis animation by Lucas V. Barbosa

– http://commons.wikimedia.org/wiki/File:Fourier_transform_time_and_frequency_domains.gif

• Cognitive Psychology books of interest

• “Evidence-Based Technical Analysis” by David Aronson

• “Thinking Fast and Slow” by Daniel Kahneman


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X-Raying the Black Box - afta-dfw.org Dickover X-Raying the Black Box - Feb... · X-Raying the Black Box By Mel Dickover AfTA, Dallas, Texas Feb 17, 2015