The Market Isn't Random — It's Patterned

• During a live beta test in January-February, the top 100 trades averaged 2.6% gains in 9 trading days versus 0.4% for the S&P 500 (approximately 7X market returns, equivalent to 73% annualized)
• Example signals include Invesco gaining 18.8% in 11 days and Lam Research gaining 11.4% in 15 days based on specific technical indicator combinations like Bollinger Percent B and Money Flow Index
• Options trades using these signals reported results ranging from 126% (Caterpillar in 72 hours) to 1,082% (Generac in 33 days) during the testing period

Summary

Key Announcement:

TradeSmith CEO Keith Kaplan introduces a new AI-powered trading system that identifies high-probability trade setups by analyzing 2.09 million potential trades daily. The company is hosting a launch event on Wednesday, April 22 at 10 a.m. Eastern.

Main Company & Background:

TradeSmith is a Baltimore-based financial technology firm with 65 employees, an $8 million annual budget, and over 134,000 users across 86 countries managing $29 billion in assets. The company previously developed TradeStops risk-management software.

System Performance:

• One-year backtest showed the signal-based portfolio outperformed the S&P 500 by approximately 3-to-1
• Live internal beta test (January-February) showed 2.6% average gain in 9 trading days versus 0.4% for the S&P 500—roughly 7X market returns
• Annualized equivalent return of 73%
• Signals demonstrate 90%+ historical accuracy when specific factor combinations align

Example Trades:

• Invesco Ltd.: 18.8% gain in 11 days using Bollinger Percent B and Money Flow Index signals
• Lam Research: 11.4% gain in 15 days (86% historical accuracy) based on 200-day moving average and pre-holiday timing

Options Results:

• Caterpillar: 126% in 72 hours
• Nvidia: 129% in 5 days
• Lockheed Martin: 365% in 30 days
• HCA Healthcare: 461% in 13 days
• Generac Holdings: 1,082% in 33 days

Market Implications:

The system suggests markets follow repeatable patterns rather than random movements, identifying profitable setups across various market conditions (bull/bear markets, crashes, recoveries) by combining technical indicators, price patterns, and market conditions that human analysts wouldn't typically correlate.