Indicators - Interactive Brokers Applications and Pitfalls Adam Grimes CIO, Waverly Advisors, LLC ....
Transcript of Indicators - Interactive Brokers Applications and Pitfalls Adam Grimes CIO, Waverly Advisors, LLC ....
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Indicators Applications and Pitfalls
Adam Grimes CIO, Waverly Advisors, LLC
October 6, 2015
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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 Guidelines for use
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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
The early history of technical analysis shows people experimenting with interesting calculated measures.
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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
One of the simplest models is a mean (average).
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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 – Strengths and weaknesses – Descriptive vs. predictive power
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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 following the dot.com crash, possibly as a backlash to indicators
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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
Understand the application
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Classification Systems Overlay or sub-chart Bounded or unbounded
– Bounded: oscillators
Momentum or OB/OS
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Overlay / Window Overlay Moving averages Bands Trendlines Parabolic SAR Trailing stops Levels
Window Stochastic MACD ROC Etc.
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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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Momentum / OB/OS Momentum ADX MACD ROC
Overextension Stochastics RSI %R
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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.
Create discipline for the trader and the trading program
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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
Analysis: Market Structure, Price Action, and Trading Strategies. Adam Grimes. Wiley (2012))
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MACD Construction
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MACD Line Crossings
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MACD Slow Line
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Modified vs. Standard MACD
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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 intuition about the statistics behind the tool. – There are mathematical ways to do this, but seeing the indicator
move can help build intuition.
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SMA vs XMA Trend Shift
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SMA vs XMA Inflections
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Surprises?
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SMA vs EMA Shock Response
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MACD Artifacts
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One Shock, Multiple Inflections
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Artifacts: Not Just Theory
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Understanding MACD Divergence
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Understanding MACD Divergence
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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.
You don’t have to understand all indicators like this, (only the ones you are going to use!)
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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
buy and hold?
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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
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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 Biggest pitfall is probably in taking too many cuts
through the data, refining the question each time. – Subtle, but powerful, way to fit the question to the data.
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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
Be careful of assumptions. Understand your tools and markets.
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Keltner Channel Fade
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RSI in Rangebound Market
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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
– Can incorporate open filters to increase edge
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Waverly Advisors Extension
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Published daily in PDF & .XLS format
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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.
Multiple timeframe considerations need to be considered.
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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 http://adamhgrimes.com
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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.
– Momentum traders—swing traders—investors
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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 [email protected]