| Date | Time | Location | Price* | Registration Deadline** |
*Prices do not include VAT, GST, or any other local taxes. All applicable taxes will be added to the invoice.
**Please register by the deadline to help us ensure sufficient attendance and avoid postponing the course.
Taught by Mr. Richard Weissman, a world-renowned author and technical trader, this two-day live course is filled with techniques, analysis and insights that only his 30 plus years of trading experience can bring. This 2-day program will provide you with a comprehensive understanding of various strategies employed in the field of advanced technical analysis, and how and when to use them.
Topics covered include:
This course applies to individuals at all levels of the commodities industry including producers, consumers, physical traders, derivatives traders and trade support staff. Professionals from: commercial hedgers, marketers, end-users, banks, hedge funds, employees of futures exchanges, futures commission merchants, data vendors, pricing publications and government agencies.
Day One
Session 1: Behavioral Finance and Technical Analysis
Session 2: Risk Management, Volatility and Time Cycle studies
Session 3: Developing Trading Models with CQG Software
Session 4: Developing Mechanical Trading Systems
Using this real-time trading system development simulation attendees will analyze a commodity, develop a market opinion regarding which type (trending, mean reverting) of trading system will outperform over the next twenty-four hours and learn how other systems performed.
Session 5: Regret Minimization Techniques
What can we do to make it easier to follow our trading rules? Since the most common reason traders abandon discipline is regret over losses and /or missed opportunities this session offers various methods to counter these self-destructive tendencies. Particular emphasis is placed on techniques to minimize regret for trend-following as well as countertrend traders.
Session 6: Applying the Theories: Mechanizing Elliott Wave, Candlesticks and Trendlines
This session applies the theories discussed in the prior session to specific areas of subjective (non-mathematical) technical analysis including fuzzy logic to candlestick formations, applying neural nets and genetic algorithms to mechanical trading systems and mechanizing Elliott Wave with Tom Joseph’s Elliott Wave Oscillator and Profit Taking indicator.
Day Two
Session 1: DeMark Indicators and Trading Systems
This session explores the major indicators used by Tom DeMark including:
Session 2: Optimization, Curve Fitting, Backtesting and Forward Testing
This session examines various methods to ensure the robustness of a mechanical trading system including backtesting and forward testing. Emphasis will be placed on elimination of suboptimal parameter sets, data integrity issues, liquidity risk as well as various curve-fitting problems (parameter and data curve-fitting). The session closes with an in-depth examination of backtesting and forward testing as well as the development and implementation of trading system “failsafes” based on losses, drawdowns and paradigm shifts.
Session 3: Trader Psychology and Matching the Trading System to the Trader
This session explores trader psychology, the importance of even-mindedness, non-attachment to the trade’s outcome and elimination of conflicting beliefs regarding trading for a living. In addition, it dispels the myth of trading systems as a, “one size fits all” proposition through a detailed exposition of various long-term, intermediate-term, swing and day trading systems. Special focus is placed on psychological issues such as, “fading the crowd”, “screen burnout”, “buying new highs” and selling new lows”. We’ll close the session by examining realistic performance expectations for various systems based on peak-to-valley equity drawdowns, winning percentages, average trade duration, average “flat” time and longest drawdown duration prior to achievement of new equity highs.
Session 4: Short-term Trading for Professional Speculators
This session explores the primary types of timing inefficiency edges and how both swing and day traders seek to exploit them, with a focus on common strategies used by hedge funds, their strengths and limitations, and practical methods for mitigating those weaknesses. These techniques will then be applied in a short-term, real-time simulated trading exercise.
Session 5: Enhancing Performance by Combining Non-Correlated Trading Systems
The session shows how performance can be enhanced through three types of diversification: asset class, parameter set and trading system. We then explore the pros and cons of each type of diversification method. Finally, we examine how combining uncorrelated trading systems allows us to expand beyond our natural comfort zones as traders, thereby allowing us to systematically overcome psychological limitations regarding the type of market action we can capitalize on.
Session 6: Algorithmic Trading
We’ll close the course with an exploration of how modern quantitative trading transforms traditional technical analysis into a systematic, data-driven process. Rather than treating indicators as simple buy/sell signals, this session reframes them as feature inputs within statistical and machine learning models, emphasizing conditional probabilities, nonlinear interactions, and market context. Participants will learn how professional traders convert chart patterns into precise, testable rules, apply volatility-based risk normalization using tools like ATR, and distinguish between structural edges (such as trend-following) and short-term timing opportunities driven by microstructure inefficiencies. The session also covers robust system design—highlighting diversification, position sizing, and regime adaptability—along with execution techniques like VWAP and TWAP. The key takeaway is that in algorithmic trading, technical analysis is no longer visual or discretionary, but a scalable framework of quantified rules and probabilities designed to capture repeatable edges across markets.