Indicators. Applications and Pitfalls. Adam Grimes

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1 Indicators Applications and Pitfalls Adam Grimes CIO, Waverly Advisors, LLC October 6, 2015

2 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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4 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.

5 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?

6 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).

7 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?

8 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

9 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)

10 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

11 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

12 Guiding Principles Understand the tool How it is calculated What is measures How it will react in extremes Understand the application

13 Classification Systems Overlay or sub-chart Bounded or unbounded Bounded: oscillators Momentum or OB/OS

14 Overlay / Window Overlay Moving averages Bands Trendlines Parabolic SAR Trailing stops Levels Window Stochastic MACD ROC Etc.

15 Bounded / Unbounded Bounded (Oscillators) Stochastics RSI Williams %R Unbounded MACD ROC CCI ADX Band width Volume Volatility measures

16 Momentum / OB/OS Momentum ADX MACD ROC Overextension Stochastics RSI %R

17 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.

18 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

19 Looking Deeper Look at how indicator is constructed Look at how it responds in different market action Consider performance stats

20 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))

21 MACD Construction

22 MACD Line Crossings

23 MACD Slow Line

24 Modified vs. Standard MACD

25 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

26 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.

27 SMA vs XMA Trend Shift

28 SMA vs XMA Inflections

29 Surprises?

30 SMA vs EMA Shock Response

31 MACD Artifacts

32 One Shock, Multiple Inflections

33 Artifacts: Not Just Theory

34 Understanding MACD Divergence

35 Understanding MACD Divergence

36 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!)

37 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.

38 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?

39 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.

40 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.

41 100 Day Channel Breakouts

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45 Fading Bollinger Bands

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48 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.

49 Keltner Channel Fade

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52 RSI in Rangebound Market

53 RSI in Trending Market

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55 Slow Stochastic

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57 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.

58 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

59 Waverly Advisors Extension

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62 Published daily in PDF &.XLS format

63 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

64 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.

65 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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69 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 2014 by Waverly Advisors, LLC. All rights reserved. No part of this document may be reproduced or transmitted in any form or by any means without the express written consent of Waverly Advisors.

70 Adam Grimes Managing Partner, Chief Investment Officer Waverly Advisors 5607 Pittsford-Palmyra Rd Pittsford, NY Chris Noye Managing Partner (607) by Waverly Advisors, LLC. All rights reserved. No part of this document may be reproduced or transmitted in any form or by any means without the express written consent of Waverly Advisors.