Indicators. Applications and Pitfalls. Adam Grimes

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Indicators

Applications and Pitfalls

Adam Grimes

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Outline



A little history lesson



_{What indicators are and what they can do}

–

even more important—what they can not do



How to look at indicators



Statistical studies

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© 2015 by Adam Grimes, LLC. All rights reserved. No part of this publication may be reproduced, distributed, or transmitted in any form or by any means except in the case of brief quotations embodied in critical reviews and certain

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In the Beginning…



There was price, and people started writing prices

down. They wanted to know the future of those

prices.



Soon, different types of graphs were used to show

price histories.

–

This started earlier than most people might think

–

Price histories date back to Antiquity with good history

from medieval Europe

–

Charting techniques were used in Japan in the 1700s

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Early Efforts



Fibonacci ratios



_{Pivot points}



Geometrical relationships (squares, counts, etc.)



_{Price channels}



Moving averages



Percent above/below moving averages



Respect how difficult this work was in the

pre-computer era…



But how much more difficult was it to evaluate?

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Let’s Think About Models



A model is a simplification of reality

–

Models include assumptions

–

Models lose detail



Models can be good or bad, depending on a number

of factors

–

Assumptions

–

“Goodness” of the model

–

Type of situation being modeled

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How Useful is a Model?



5 people: 13, 22, 31, 33, 45 years old

–

Mean is 28.8

–

How useful is this number?

–

How many people are “close” to this age?



Now, add a 1,000,000 year old mummy

–

The mean is now 166,690.7 years.

–

How many people are “close” to this age?

–

How useful is this model?



The medians for these datasets are 31 and 32 years

–

More useful? Less useful?

–

What is lost?

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More About Models



Models can vary in complexity.



_{In some situations, more complex is not better.}

–

In financial markets, complexity makes it easier to fit the

model to the data with no increase in predictive power



_{(The demise of the neural nets)}



We must always understand the models.

–

Assumptions

–

Techniques

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The Personal Computer Arrives



And indicators proliferate…

–

MACD (1970’s & 1986)

–

Relative Strength Index (1978)

–

Average Directional Index (1978)

–

Commodity Channel Index (1980)

–

Parabolic SAR (1978)

–

Stochastic (1950s?)

–

Bollinger Bands (1980s)

–

McClellan Oscillator (1960s)

–

TRIN (late 1970s)

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Later Developments



More computing power lead to more complexity

–

Neural nets, algorithms, etc.



Digital signal analysis tools were applied to market data.

–

Cycles in markets are problematic (more on that to come).



_{Most indicators rehash the same data, so there were}

fewer innovations as time went on.

–

Most of the indicators you know were probably on that list from

pre-1990s



_{“Price action” trading became an internet phenomenon}

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Where Are We Today?



Many tools are readily available.

–

Every charting package (even free) has a good selection of

indicators



_{We have the perspective of history}

–

Limitations of indicators are clear



Avoid the ongoing search for the “perfect” technical

indicator.

–

Use must fit your trading approach

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Guiding Principles



Understand the tool

–

How it is calculated

–

What is measures

–

How it will react in extremes

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Classification Systems



Overlay or sub-chart



_{Bounded or unbounded}

–

Bounded: oscillators



Momentum or OB/OS

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Bounded / Unbounded

Bounded (Oscillators)



Stochastics



_RSI



_{Williams %R}

Unbounded



MACD



_ROC



_CCI



_ADX



_{Band width}



Volume



Volatility measures

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What Can Indicators Do?



Highlight aspects of price action that might not be

readily visible on chart.



Separate what is meaningful from what is not.



_{Reduce market action to a precise set of trading}

rules.



Normalize and compare across many different

markets.

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What Can Indicators

Not Do?



There are no “magic lines”—there’s no way to create

something from nothing.



Indicators cannot create an edge where none exists

in the market.



Cannot generate consistent, winning trades based on

the indicator alone

–

Indicators are tools.

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Looking Deeper



Look at how indicator is constructed



_{Look at how it responds in different market action}



Consider performance stats

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Example: Modified MACD



MACD line: 10 period SMA – 3 period SMA



_{Signal line: 16 period SMA of the MACD line}



Plot below prices with a zero line



(Charts are from The Art and Science of Technical

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MACD Construction

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MACD Slow Line

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Differences Due to Averages



We need to understand averages better



_{Simple Moving Average (SMA)}

–

Simple average of prices over time window

–

Subject to “drop off” effect

–

All prices equally weighted



_{Exponential Moving Average (EMA or XMA)}

–

Weights for each older datapoint decrease exponentially,

but never reach zero

–

Old data is never dropped (but it fades away)

–

“Period” does not exactly compare to SMA

–

Generally tends to be smoother and more front-weighted

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“Test Room” for Indicators



_{Plot indicators}



Create specific types of price series so we can understand

the action of the indicators better

–

Simple trends

–

Cycles

–

Large price shocks



Most charting packages can take ASCII data, so generate

csv files for price series.



_{This is an important tool for building understanding and}

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SMA vs XMA Trend Shift

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Surprises?

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MACD Artifacts

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Artifacts: Not Just Theory

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other noncommercial uses permitted by copyright law. For other permission requests, contact the author.

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Understanding Indicators



Most people start by learning indicator rules

(crossings, divergence, etc.)

–

But where do those rules come from?

–

What does the indicator actually measure?

–

How does it work?

–

Do the rules work?



Work to understand the tool and the measure first,

then figure out applications.

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Performance Stats



_{Any statistical test we do tests both the market and the}

structure we impose on it.



With indicators, there’s another step: the indicator is

structure, but then we impose rules on the indicator.



_{Do good tests}

–

Appropriate datasets and testing tools

–

Avoid testing many variations of the same rule.



_{e.g., OB/OS at 70/30, then 80/20}

–

Avoid too many combinations of rules and tools.

–

Out of sample data?



We are looking for simple, high-level tendencies to see if

a tool has an edge.

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Event Study Methodology



Define test universe.

–

Represent all important asset classes.

–

Address several volatility regimes.

–

Adjust for baseline drift.



Compute summary stats for each asset class.

–

Baseline drift is particularly important = hurdle rate.

–

If you’re not making more than the baseline, why not just

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Event Study (cont.)



Define a precise condition that will result in a trading

signal.

–

Symmetrical for buy and sell.



Work through each bar of the universe looking for

the condition.



Record returns for each bar following the condition.



Combine all N+1, N+2, … returns to get a composite

for each bar following the signal.



Create excess return measure (signal – baseline) for

each bar.

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Simple, High-Level Tests



Looking for underlying tendency and true statistical

edge.



Avoid “full system” tests or tests that show P&L



There are things these tests could miss

–

Statistical significance is not the last word

–

Economic significance must be considered

–

Tools might show an edge in combination that do not show

an edge separately

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100 Day Channel Breakouts

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Fading Bollinger Bands

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Any Tool to Any Market?



Traditional TA says we can apply any tool to any

market.



Quantitatively, we can see that the balance of mean

reversion and momentum appears to be very

different in asset classes.

–

This is a persistent element of market behavior

–

Commodities and currencies “trend” better than stocks

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Keltner Channel Fade

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RSI in Trending Market

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Slow Stochastic

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Indicators vs. Models



Indicators are primarily visual, and can be difficult to

test.



Another possibility is to use trading models (simple

systems) as inputs, either to other systems or to your

own discretion.

–

Weighting is a question, but much of the research on

factors in expert analysis shows that a simple binary

weighting is most effective.

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Waverly Advisors’ Extension



An overbought/oversold algorithm



_{Evaluates both extension and momentum on}

multiple timeframes



_{Looks at derivatives of volatility and rate of change as}

well



Simple, robust model



Gives signals on close, but also strong edge on open

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Waverly Advisors Extension

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Using Indicators



There are many indicators out there.

–

Some have an edge and some don’t

–

All can only reveal things that are already in prices



Indicators should be incorporated into your trading plan

–

Consistency is key

–

Looking at new indicators while in a trade is a classic early

warning of an impending break of discipline

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Combining Tools



Many indicators show highly “correlated” pictures



_{Using correlated inputs into a decision model is a bad}

idea

–

Increases apparently confidence without increasing

predictive value



Multiple indicators can lead to “paralysis by analysis”



There may be opportunities to combine indicators,

but make sure you understand what they are

showing and how they work.

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General Guidelines



Understand your edge

–

The only way to do this is through some type of statistical

analysis



Understand how each piece of your trading program

contributes to your edge

–

This can be difficult to tease apart

–

Generally, much more can be quantified than most people

think



_{Specific rules for indicator line crossings,}

divergences, etc. are perhaps the least important

part of the puzzle

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My Blog

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Waverly Advisors’ Research



Specific systems, broad tendencies, and actionable

ideas in major liquid markets.

–

Futures

–

Currencies

–

Stocks (indexes and individual names)



_{Both trend-following and counter-trend components.}



Applicable to traders working on all timeframes.

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Waverly Advisors, LLC:

Research Products

Tactical Playbook

– Available on Interactive Brokers

–

Written for the active trader on the daily/weekly timeframes

–

Exact trade recommendations



Hybrid systematic-discretionary methodology

–

In-depth technical “drill down” into a set of markets.

–

Bigger-picture overview of all liquid asset classes.

Tactical Portfolio Outlook

– Available on Interactive Brokers

–

Written for the longer-term manager



_{Addresses both the allocator and the longer-term active trader.}

–

Emphasis on executing with ETFs in a long-only and long-short environment

–

Focus on Equities, Equity Sectors, and other asset classes

–

Macro perspective on risk factors and major economic events.

Options Market Outlook

– Contact Waverly Directly

–

Proprietary, quantitative analysis of options market

–

Incorporates both volatility and directional analysis

–

Macro risk factors and cross-asset perspective

–

Actionable trade ideas

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Waverly Advisors

5607 Pittsford-Palmyra Rd. 1034

Pittsford, NY 14534

(607) 684-5300

www.waverlyadvisors.com

[email protected]

Adam Grimes

Managing Partner, Chief Investment Officer

[email protected]

Chris Noye

Managing Partner