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By Scott Juds – Sumgrowth Strategies AAII Phoenix Chapter by Scott Juds –PresentedSumGrowth by ScottStrategies Juds – Sept. 2019 December 12, 2019 President & CEO, SumGrowth Strategies 1 Disclaimers • DO NOT BASE ANY INVESTMENT DECISION SOLELY UPON MATERIALS IN THIS PRESENTATION • Neither SumGrowth Strategies nor I are a registered investment advisor or broker-dealer. • This presentation is for educational purposes only and is not an offer to buy or sell securities. • This information is only educational in nature and should not be construed as investment advice as it is not provided in view of the individual circumstances of any particular individual. • Investing in securities is speculative. You may lose some or all of the money that is invested. • Past results of any particular trading system are not guarantee indicative of future performance. • Always consult with a registered investment advisor or licensed stock broker before investing. 2 Merlyn.AI Prudent Investing Just Got Simpler and Safer The Plan: • Brief Summary of our Base Technology • How Artificial Intelligence Will Help • A Summary of How Merlyn.AI Works • The Merlyn.AI Strategies and Portfolios • Using Merlyn.AI within Sector Surfer • Let’s go Live Online and See How Things Work 3 Company History 2010 2017 2019 Merlyn.AI Corp. News Founded Jan 2019, Raised $2.5M, Exclusive License from SGS to Create & Market Merlyn ETFs Solactive US Bank RBC Calculator Custodian Publisher Market Maker Alpha Architect SGS Merlyn.AI ETF Advisor SectorSurfer SGS License Exemptive Relief NYSE AlphaDroid Investors Web Services MAI Indexes ETF Sponsor Compliance Quasar Marketing Distributor Cable CNBC Mktg. Approval Advisor Shares SEC FINRA G.Adwords Articles First Proved Momentum in Market Data Narasiman Jegadeesh Sheridan Titman Emory University U. of Texas, Austin Academic Paper: “Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency” (1993) Signal-to-Noise Ratio Controls the Probability of Claude Shannon Making the Right Decision National Medal of Science, 1966 Proved Signal-to-Noise Ratio Controls the Probability of Making the Right Decision Claude Shannon National Medal of Science, 1966 Matched Filter Theory Design for Optimum Signal-to-Noise Ratio J. H. Van Vleck Noble Prize, 1977 Think Outside of the Box Someplace to Start Designed for Performance Think Outside of the Box J. H. Van Vleck Noble Prize, 1977 Someplace to Start Designed for Performance Differential Signal Processing Removes Common Mode Noise (Relative Strength) Samuel H. Christie Royal Society 1836 5 Years Full Span Wheatstone Bridge Sectors Provide Power Strokes Market Economic Cycle Cycle The Bear Market Problem S&P 500 50 % 50 65 % 65 1991 2018 13 How Fast Can StormGuard Work? 14 15 ---- Sell Low ---- Buy High ---- Sell Low The Death Cross Problem Cross The Death 50d Moving Average Crosses the 200d Moving the 200d Average Moving Crosses Average 50d Moving Why is StormGuard-Armor Better? It Analyses Three Different Kinds of Market Behavior. It Incorporates Event Detection, not Simply Timing Adjustments. 16 StormGuard-Armor Charts Better Performance: • Three Market Views • Twelve Separate Measures • Not Shifting the Problem 17 StormGuard - Armor Sneak Incorporating Price, Highs/Lows and Volume Data Preview Utilizing Matched Filter Theory, PID Algorithms & Fuzzy Logic Coming Spring 2016 10000 S&P-DA SG-Std SG-Armor 1000 S&P-DA SG-Std SG-Armor Ann. Return 9.9% 11.2% 13.3% Std. Deviation 16.9% 12.3% 10.4% Sharpe Ratio 0.58 0.91 1.28 Time In Market 100% 83% 73% Avg. Trades/Year 0.00 0.58 1.57 100 1991 1994 1998 2001 2005 2008 2012 2015 1989 1990 1992 1993 1995 1996 1997 1999 2000 2002 2003 2004 2006 2007 2009 2010 2011 2013 2014 2016 18 What is a Smoke Alarm For? Is it Perfect? ? ? ? 1991 2018 Integrated Bear Market Strategy This is Why its Needed StormGuard-Armor + BMS-A No SG-Armor The Value is Obvious. No BMS-A 21 StormGuard Evolution StormGuard-Armor(+) Developed in 2016, (2018) From SG-Armor Web Page Three Market Views Signal Danger Better • Improved Exit Decisions StormGuard-Armor More • Improved Re-Entry Decisions Is Smarter – Not Faster Information • Exit to a Bear Market Strategy 22 Black Swan Events January 1950 to April 2020 Black Swan Mitigation (Required if Not Vaccinated) Suddenly… Before You Know It… • A Back Swan Is Coming at You • The Back Swan Bites Your Butt Ouch! Black Swan Mitigation (Required if Not Vaccinated) Suddenly… Before You Know It… • A Back Swan Is Coming at You • The Back Swan Bites Your Butt • And It’s Got You by Your Wallet Black Swan Mitigation (Required if Not Vaccinated) Suddenly… Before You Know It… • A Back Swan Is Coming at You • The Back Swan Bites Your Butt • And It’s Got You by Your Wallet Mitigation Means Taking Action to Keep Your Wallet and Letting the Black Swan Just Fly Away. Black Swan Mitigation (Required if Not Vaccinated) Suddenly… Don’t Sell to Before You Know It… the Black Swan • A Back Swan Is Coming at You • The Black Swan The Back Swan Bites Your Butt Always Flies Away • And It’s Got You by Your Wallet AVOID Mitigation Means Dec 2018 Whipsaw Losses Taking Action to Keep Your Wallet and FED Shock Letting the Black Swan Just Fly Away. Mitigation Response #1 Don’t Lock In Whipsaw Losses Stocks notched their fastest bear market on record Morgan Stanley SG-Delta MSI indicated oversold before SG-Armor triggered. Translation Don’t Trigger SG-Armor if Delta MSI indicates Oversold Mitigation Response #2 Don’t Lock In Technical Whipsaw Losses Translation Oversold … Mean Reversion … Buy the Dips Inverse Trend Order Best Predicts Next Month’s Return Which Kind of Recovery? L-U-V? ..... It Depends Not All Boats Rise Together Bad Breadth is Good... for Momentum Strategies Mitigation Response Deployment Status March 20, 2020 Deployment of Both Complete Throughout SectorSurfer, AlphaDroid and Merlyn.AI Mitigation #1 Mitigation #2 Oversold SG-Armor Trigger Block Select via Inverse Trend Following Oversold Vaccination Response Real-Time BS Detection Black Swans are Like an Earth Quake. 1. When VIX >25, Move to Treasury 2. Otherwise do Momentum Stuff. SWAN owns market and treasury futures SSS6 owns 50% SPY and 50% TLH (7-10yVIX treasury)Volatility Vaccination Response Real-Time BS Detection Vaccination Deployment Status Complete for SectorSurfer & AlphaDroid Vaccination Deployment Status Complete for SectorSurfer & AlphaDroid Enabling SwanGuard Let’s Go Online... For a Bit... 12-Month SMA Sector Strategy FWPT DEMA Strategy in painted path. FWPT DEMA Strategy w/ Death Cross to Cash FWPT DEMA Strategy w/ AG-Armor to Cash FWPT DEMA Strategy w/ AG-Armor to BND FWPT DEMA Strategy w/ AG-Armor to TLH FWPT DEMA Strategy w/ AG-Armor to BMS-W FWPT DEMA Strategy w/ AG-Armor to BMS-G These Guys Got us Here Is There More? What About Selection Bias? Who Needs XLV-Healthcare and XLE-Energy? . Merlyn.AI Is a Genetic Algorithm Layered on Top of a Strategy Genetic Algorithm on Top Why? To Evolve its Set of Funds Each Month Why? To Remove Hindsight Selection Bias Artificial Intelligence Algorithms Types of machine learning algorithms[edit] •Reinforcement learning Bayesian[edit] Semi-supervised learning[edit] •Almeida–Pineda recurrent backpropagation •Repeated incremental pruning to produce error reduction (RIPPER) Bayesian statistics Semi-supervised learning •ALOPEX •Rprop •Bayesian knowledge base •Active learning – special case of semi-supervised learning •Backpropagation •Rule-based machine learning •Naive Bayes Generative models •Bootstrap aggregating •Skill chaining •Gaussian Naive Bayes •Low-density separation •CN2 algorithm •Sparse PCA •Multinomial Naive Bayes •Graph-based methods •Constructing skill trees •State–action–reward–state–action •Averaged One-Dependence Estimators (AODE) •Co-training •Dehaene–Changeux model •Stochastic gradient descent •Bayesian Belief Network (BBN) •Transduction •Diffusion map •Structured kNN •Bayesian Network (BN) Deep learning[edit] •Dominance-based rough set approach •T-distributed stochastic neighbor embedding Decision tree algorithms[edit] Deep learning •Dynamic time warping •Temporal difference learning Decision tree algorithm •Deep belief networks •Error-driven learning •Wake-sleep algorithm •Decision tree •Deep Boltzmann machines •Evolutionary multimodal optimization •Weighted majority algorithm (machine l •Classification and regression tree (CART) •Deep Convolutional neural networks •Expectation–maximization algorithm •Iterative Dichotomiser 3 (ID3) •Deep Recurrent neural networks •FastICA •C4.5 algorithm •Hierarchical temporal memory •Forward–backward algorithm Supervised learning •C5.0 algorithm •Generative Adversarial Networks •GeneRec •AODE •Chi-squared Automatic Interaction Detection (CHAID) •Deep Boltzmann Machine (DBM) •Genetic Algorithm for Rule Set Production •Artificial neural network •Decision stump •Stacked Auto-Encoders •Growing self-organizing map •Association rule learning algorithms •Conditional decision tree Other machine learning methods and problems[edit] •HEXQ • Apriori algorithm •ID3 algorithm •Anomaly detection •Hyper basis function network • Eclat algorithm •Random forest •Association rules •IDistance •Case-based reasoning •SLIQ •Bias-variance dilemma •K-nearest neighbors algorithm •Gaussian process regression Linear classifier[edit] •Classification •Kernel methods for vector output •Gene expression programming Linear classifier • Multi-label classification •Kernel principal component analysis •Group method of data handling (GMDH) •Fisher's linear discriminant •Clustering •Leabra •Inductive logic programming •Linear regression
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