Merlyn.AI Prudent Investing Just Got Simpler and Safer

by Scott Juds October 2018 1 Merlyn.AI Prudent Investing Just Got Simpler and Safer

We Will Review: • Logistics – Where to Find Things • Brief Summary of our Base Technology • How Artificial Intelligence Will Help • Brief Summary of How Merlyn.AI Works • The Merlyn.AI Strategies and Portfolios • Importing Strategies and Building Portfolios • Why AI is “Missing in Action” on Wall Street?

2 Merlyn.AI … Why It Matters

3 Simply Type In Logistics: Merlyn.AI

4 Videos and Articles Archive 6 Merlyn.AI Builds On (and does not have to re-discover) Other Existing Knowledge

It Doesn’t Have to Reinvent All of this From Scratch Merlyn.AI

Builds On Top of SectorSurfer First Proved Momentum in Market Data

Merlyn.AI Accepts This Re-Discovery Not Necessary

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) Formally Confirmed Momentum in Market Data

Merlyn.AI Accepts This Re-Discovery Not Necessary

Eugene Fama Kenneth French Nobel Prize, 2013 Dartmouth College “the premier market anomaly” that’s “above suspicion.”

Academic Paper - 2008: “Dissecting Anomalies” Proved Signal-to-Noise Ratio Controls the Probability of Making the Right Decision Claude Shannon National Medal of Science, 1966

Merlyn.AI Accepts This Re-Discovery Not Necessary Matched Filter Theory

Design for Optimum J. H. Van Vleck Signal-to-Noise Ratio Noble Prize, 1977 Think Outside of the Box

Merlyn.AI Accepts This Re-Discovery Not Necessary

Someplace to Start Designed for Performance Differential Signal Processing Removes Common Mode Noise (Relative Strength) Samuel H. Christie Royal Society 1836 Merlyn.AI Accepts This Re-Discovery Not Necessary 5 Years Full Span

Wheatstone Bridge Sectors Provide Power Strokes

Merlyn.AI Market Economic Cycle Cycle Accepts This Re-Discovery Not Necessary StormGuard-Armor Detect the Onset of Bad Markets

Know When the Market is Safe: Risk-On vs Risk-Off

Merlyn.AI Accepts This Re-Discovery Not Necessary These Guys Got us Here

Is There More? What About Selection Bias? Who Needs Technology – Financials – Gold? Extreme Selection Bias 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

Why? To Achieve Better Future Performance Merlyn.AI Is a Genetic Algorithm Layered on Top of a Strategy

Genetic Algorithm on Top This Means Fund Selection is Automated

This Means You Must Submit to a Higher Power

Merlyn.AI What is Artificial Intelligence? AI Definition

Wikipedia Artificial intelligence (AI) is the ability of a machine to perceive its environment and take actions to maximize its chance of success at some goal.

Applied to Investment Can a machine perceive its environment (sector rotation, bull / bear markets) and take action to maximize returns and minimize risk? We say YES! 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 •Data Pre-processing •Linde–Buzo–Gray algorithm •Instance-based learning •Logistic regression •Empirical risk minimization •Local outlier factor •Lazy learning •Multinomial logistic regression •Feature engineering •Logic learning machine •Learning Automata •Naive Bayes classifier •Feature learning •LogitBoost •Learning Vector Quantization •Perceptron •Learning to rank •Manifold alignment •Logistic Model Tree •Support vector machine •Occam learning •Minimum redundancy feature selection •Minimum message length (decision trees, decision graphs, etc.) Unsupervised learning[edit] •Online machine learning •Mixture of experts • Nearest Neighbor Algorithm Unsupervised learning •PAC learning •Multiple kernel learning • Analogical modeling •Expectation-maximization algorithm •Regression •Non-negative matrix factorization •Probably approximately correct learning (PAC) learning •Vector Quantization •Reinforcement Learning •Online machine learning •Ripple down rules, a knowledge acquisition methodology •Generative topographic map •Semi-supervised learning •Out-of-bag error •Symbolic machine learning algorithms •Information bottleneck method •Statistical learning •Prefrontal cortex basal ganglia working memory •Support vector machines Artificial neural networks[edit] •Structured prediction •PVLV •Random Forests Artificial neural network • Graphical models •Q-learning •Ensembles of classifiers •Feedforward neural network Logic learning machine • Bayesian network •Quadratic unconstrained binary optimization • Bootstrap aggregating (bagging) •Self-organizing map • Conditional random field (CRF) •Query-level feature • Boosting (meta-algorithm) Association rule learning[edit] • Hidden Markov model (HMM) •Quickprop •Ordinal classification Association rule learning •Unsupervised learning •Radial basis function network •Information fuzzy networks (IFN) •Apriori algorithm •VC theory •Randomized weighted majority algorithm •Eclat algorithm How SumGrowth Will Use AI To Perceive the environment and take action to maximize success.

Adaptively changing the algorithm FWPT: Forward Walk based on the past character of the Progressive Tuning Old data. Walks through out-of-sample data for its buy/sell decisions.

Employs Fuzzy Logic to evaluate a composite of 12 measures of the StormGuard - Armor Old market’s character to determine current investment safety.

Uses a Genetic Algorithm to evolve Merlyn.AI the candidate funds in a population FWPP: Forward Walk New Progressive Picking of momentum strategies to eradicate remnants of hindsight selection bias. Removing Hindsight Selection Bias

Funds Picked with In-Sample Data, But Used with Out-of-Sample Data

3. Employ a Genetic Algorithm “Forward-Walk Progressive Picking” How Our Genetic Algorithm Works How Our Genetic Algorithm Works Consider This Analogy to Humans

Genetic Evolution: Mutation Crossover

Human Strategy What are the Gene Mutations Where do the Gene Options Come From?

Scraped From ETF.com How Our Genetic Algorithm Works

Population of 12 Population of 12

Each month mutant and Only children with better crossover children are made. “fitness” than their parents survive by replacing the parent.

Children Evolution History for Member-1 How Our Genetic Algorithm Works Evolution History for Member-6 AIGenetic Momentum Portfolio Manager

Monthly Choice Made by SOS Vote (Like a Strategy-of-Strategies) 300x Time Lapse Merlyn.AI Strategy Fund Candidates for Bonds Aye Bonds Aye

. Merlyn.AI Strategy Fund Candidates for Style Box Fox Style Box Fox Merlyn.AI Strategy Fund Candidates for Factor Faves Factor Faves Merlyn.AI Strategy Fund Candidates for Global Domination Global Domination Merlyn.AI Strategy Fund Candidates for Sector Nectar Sector Nectar Importing Merlyn.AI in SectorSurfer Importing Merlyn.AI in SectorSurfer Importing Merlyn.AI in SectorSurfer Importing Merlyn.AI in SectorSurfer Importing Merlyn.AI in SectorSurfer Importing Merlyn.AI in SectorSurfer Importing Merlyn.AI in SectorSurfer Importing Merlyn.AI in SectorSurfer

All of them loaded. All trades acknowledged. Importing Merlyn.AI in SectorSurfer Importing Merlyn.AI in SectorSurfer Importing Merlyn.AI in SectorSurfer Importing Merlyn.AI in SectorSurfer Importing Merlyn.AI in SectorSurfer

. Merlyn.AI on Merlyn.AI on Merlyn.AI on Why are AI Funds M.I.A. on Wall Street?

Oct. 2017

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June 2018 Why are AI Funds M.I.A. on Wall Street? Why are AI Funds M.I.A. on Wall Street? Why are AI Funds M.I.A. on Wall Street?

August 2018

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A Distinction Without a Difference Why are AI Funds M.I.A. on Wall Street?

Most AI Research is Focused on Neural Network Deep Learning

• Can it discover the importance of Signal-to-Noise Ratio? • Can it discover Matched Filter Theory to reduce the noise? • Can it discover Differential Signal Processing to reduce the noise? • Can it discover Problem Segmentation for Bear Markets? • Can it discover Temporal Suitability tests for candidate funds?

Too Many Simultaneous Problems to Solve? Why Does Merlyn.AI Work So Well? Built On Proven Knowledge and Methods Re-Discovering Nobel Prize Concepts is Not Required

Focus On One Problem: Eliminating Hindsight Selection Bias Merlyn.AI Prudent Investing Just Got Simpler and Safer

by Scott Juds October 2018 66