Larry Connors – How To Build High-Performing Trading Strategies With AI

How To Build High-Performing Trading Strategies With AI is Larry Connors’ multi-session training on applying artificial intelligence to quantitative trading in stocks and ETFs. It contains more than 24 hours of instruction, including multiple six-hour workshops.

AI trading strategy skills

• Build quantitative trading strategies using AI tools to identify statistical edges in market data
• Apply professional alpha extraction techniques including mean reversion and RSI-based systems
• Backtest strategies across years of historical data to validate performance before risking capital
• Optimize strategy parameters without overfitting using stress-testing methods
• Develop a portfolio of systematic trading approaches that work across different market conditions
• Implement proper position sizing and risk management for live AI-driven trading

Why use AI in quantitative trading?

AI tools can help individual traders examine large datasets for patterns, test combinations of parameters and investigate potential statistical edges. Connors applies his experience developing rule-based strategies since the 1990s to these tools, adding a systematic approach to the skills of traders familiar with trading fundamentals.

Inside the trading strategy training

The opening sessions examine how quantitative researchers look for alpha and distinguish a possible market edge from noise. Examples include mean-reversion setups, Connors’ two-period RSI model and more recent AI-assisted discovery methods.

The strategy-building sessions move from constructing trading rules to testing them against historical data. Connors discusses how to evaluate results using metrics such as win rate, drawdown and Sharpe ratio, with attention to how a strategy may behave beyond its backtest.

Later modules address refinement and the risk of overfitting historical data. They examine stress tests, parameter adjustments and combining systems for different market conditions. The final sessions turn to putting a strategy into practice, including position sizing and risk management.

Training materials

The course materials include MP4 workshop recordings, strategy-building frameworks and templates, and backtesting methodology materials. Docs and PDFs accompany the videos.

About Larry Connors

Larry Connors has traded and researched financial markets for more than 40 years, beginning at Merrill Lynch in 1982. He founded Connors Research LLC and TradingMarkets.com.

He has authored or co-authored more than 25 trading books, including Short Term Trading Strategies That Work and Street Smarts with Linda Raschke. His research includes the two-period RSI and ConnorsRSI.

Who the course is for

The training is aimed at experienced traders interested in applying quantitative methods, including active traders, swing and position traders, and those focused on backtesting and optimization.

Trading involves risk, and historical results do not guarantee future performance.

Frequently asked questions

What does How To Build High-Performing Trading Strategies With AI cover?

Larry Connors covers alpha extraction, AI-assisted strategy construction, historical testing, optimization and implementation for stocks and ETFs.

Who is the course suited to?

It is intended for traders with some experience who want to add systematic, data-driven methods to their process, including active, swing and position traders.

Do I need programming experience?

Deep programming knowledge is not required for the core material. Familiarity with trading concepts is helpful.

What materials are included?

The materials include more than 24 hours of MP4 video instruction, workshop recordings, strategy frameworks and templates, and backtesting methodology materials. Docs and PDFs are also included.

What is Larry Connors’ Double 7 Strategy?

The Double 7 is a published mean-reversion system that buys when the S&P 500 closes at a seven-day low and sells at a seven-day high. The course addresses AI-driven strategy building beyond any single system.