The CMT Level III exam tests the candidate’s ability to integrate a wide range of
concepts and tools into the application of technical analysis.The Level III exam is organized into groups, most of which weave together two or more knowledge domains.
Chartered Market Technician Level 3 - Prep Course
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What will you learn ?
- 1 System Design and Testing
Assess the value and challenges of using a system for trading or investing
Compare and analyze differences between discretionary and nondiscretionary systems
Evaluate the mind-set and discipline required to develop and trade with a system
Organize the basic procedures for designing a system
Inventory types of technical trading systems
Defend the necessity of risk management protocols in a trading system
Examine critical aspects of performing system tests
Compare and evaluate standard measures of system profitability and risk
Differentiate between various methods of optimization - 2 Money and Portfolio Risk Management
Distinguish between trading strategies and money-management strategies
Evaluate the significance of the theory of runs and a martingale strategy
Model position size using risk of ruin and optimal f methods
Differentiate between diversifiable and correlated risk
Compare and analyze the various types of stops used to manage risk
Assess the minimum capital needed for trading a system - 3 System Evaluation and Testing
Choose factors for system testing including objectives, parameters and test data
Assess the use of in-sample and out-of-sample data
Evaluate optimized test results for continuity and significance using a variety of visualization methods
Explain the basics of using genetic algorithms
Illustrate the concept of robustness in a trading system
Critique the use of performance and risk metrics based on a given objective - 4 Practical Considerations
Plan for system development and testing: data, techniques, and initial evaluation of results
Assess the potential impact of price shocks and formulate plans for managing them
Assess the impact of runs and martingales on a trading system
Evaluate the trade-offs between trend-following and mean-reverting systems - 5 Risk Control
Compare risk and performance metrics derived from the following: Sharpe Ratio, Information Ratio, Treynor Ratio, Calmar Ratio, Sortino Ratio
Interpret calculations of Value at Risk (VaR)
Model position size using various capital and volatility approaches in this chapter
Compare various methods for setting stops and profit targets
Compare approaches to compounding positions
Calculate the risk of ruin
Calculate optimal f - 6 Statistical Analysis
Assess random and nonrandom trends in trading system performance
Examine sampling and sample statistics in trading
Calculate relative frequency
Organize six elements of a statistical inference problem
Differentiate between theoretical and empirical probabilities
Derive a sampling distribution - 7 Hypothesis Tests and Confidence Intervals
Differentiate between necessary and sufficient conditions
Compare the assertions of the null and alternative hypotheses
Defend why the null hypothesis should be framed as the target of a test
Prepare the data and arrange the steps for a Monte Carlo simulation
- 8 Regression
Assess values generated by regression, multiple regression and tolerance calculations
Select meaningful predictor variables for multiple regression studies based on correlation values among them and with the dependent variable - 9 Gold
Compare general correlations among gold, dollar, stocks, and indexes - 10 Intermarket Indicators
Construct relative strength studies and evaluate the results
Compare intermarket indicators described in this chapter
Prepare recommendations based on asset correlation data - 11 A Unique Way to Visualize Relative Strength
Evaluate the trend and momentum of relative strength using Relative Rotation Graphs (RRGs)
Assess relative strength using the indicators derived from the RRG concept
- 12 Fact, Fiction and Momentum Investing
Defend the use of momentum strategies using historical data
Argue against common myths about momentum strategies - 13 Analyzing the Macro-Finance Environment
Assess the business cycle, the financial cycle, and their relationship
Manage a sector rotation model based on the business and financial cycles
Use leading, coincident, and lagging indicators of economic activity - 14 Portfolio Risk and Performance Attribution
Assess the statement “total risk = volatility = standard deviation of returns”
Compare the three formulations of total risk
Defend the assertion that “diversification reduces only firm-specific risk”
Defend beta and its role in assessing portfolio risk
Employ the Sharpe and Treynor ratios for individual stocks and portfolios
- 15 Behavioral Biases
Distinguish between two types of biases: cognitive and emotional
Formulate plans to counter behavioral biases in making investment decisions
Propose methods to capitalize on the behavioral biases of other market participants
Examine the specific behavioral biases in each of those categories - 16 Investor Psychology
Inventory general behavioral aspects that impact price action
Evaluate behavioral elements that contribute to the development of chart patterns
Evaluate behavioral elements that contribute to the persistence of trends
Evaluate behavioral elements that contribute to periods of consolidation
Evaluate behavioral elements that contribute to trend reversals - 17 Are Two Heads Better than One?
Assess the negative consequences of group/committee decision making
Organize approaches to mitigating the effects of group biases - 18 The Anatomy of a Bubble
Diagram the five stages of a bubble
Assess the characteristics of each of the five stages
Assess hypothetical market environments to identify what stage they indicate - 19 De-Bubbling: Alpha Generation
Assess the three cross-section strategies that should benefit from a de-bubbling/deflationary period - 20 Behavioral Techniques
Evaluate market reactions to events: planned news releases versus price shocks
Estimate reactions to events using the volatility ratio
Assemble a COT Index and a COT Sentiment Index from Commitments of Traders (COT) data
- 21 The VIX as a Stock Market Indicator
Compare movement in the VIX and the S&P 500
Evaluate VIX and VIX futures price relationships for signals
Formulate market forecasts that include volatility as an input - 22 Hedging with VIX Derivatives
Defend the rationale behind hedging with VIX products
Propose hedge strategies using VIX options and futures
23 Advanced Techniques
Assess the relationship between price and volatility
Compare several measures of volatility
Calculate profit targets and stop-loss levels using volatility
Evaluate methods for filtering a system’s signals based on volatility
Assess how fractal, chaos, and entropy concepts may be applied to trading
Explain the basics of using neural networks
Explain the basics of using genetic algorithms
- 24 Pattern Recognition
Compare and evaluate pivot points and DeMark’s calculations for price ranges
Examine intraday data for idiosyncratic patterns in various markets
Assess the use of opening gaps as trading signals - 25 Multiple Time Frames
Evaluate chart data using Elder’s and Pring’s multiple time-frame methods
Defend Krausz’s six rules for multiple time frames - 26 Candlestick Analysis
Evaluate the strengths and weaknesses of candlestick charts
Categorize reversal and continuation candlestick patterns
Interpret the nine important price action guidelines
Assess the significance of various Japanese candlestick patterns to pinpoint reversals and breakouts
Integrate candlestick charts with other technical studies - 27 Progressive Charting
Evaluate candle patterns as they develop in a chart
Compose responses to the four questions posed at the outset of the chapter - 28 Bringing it All Together: Real-World Charts
Predict likely price action based on candlestick patterns and the overall context of the price action
Propose entry and exit points based on patterns, price action, and risk
Assess trend persistence based on candlestick patterns and the overall context of the price action - 29 Conclusions
Assess the validity of the 12 major conclusions about technical indicators the authors present
Defend the use of technical indicators when properly employed in a variety of market environments