Welcome to our four-part video series, where we take an in-depth look at a specific strategy built around the Dual Algos setup using ITG Quantum AI. In this series, we demonstrate how version 2.03 of ITG Quantum AI, along with its Reverse logic counterpart, performs when applied to the NASDAQ 100 e-mini futures market. Our test takes place during the high-volatility environment of May 2025—a period that followed an already turbulent April. This was a particularly difficult time for traders, as margin restrictions were imposed industry-wide due to ongoing market instability. Most brokerage firms, including ours, suspended intraday margin discounts for nearly two months straight—an action typically reserved for days with major economic events. This extreme risk environment provides a valuable opportunity to assess how the algorithm functions under pressure.

For the purpose of this test, we’ve implemented a 1:1 risk-reward strategy across both algorithm versions. Daily risk is capped at $7,500 per contract for e-mini accounts and $750 for e-micro contracts. The corresponding profit targets are identical, maintaining a strict balance between risk and reward. The system is configured to trade both long and short signals, following the Super Trend indicator as the directional bias. We’ve set the profit target at 50 ticks (or $250) per trade, and the scaling logic uses predefined buffer zones to control position growth. All trade entries are generated using scalping mode, and standard Super Trend settings are applied to ensure consistency in execution.

Unlike many trading strategies that concentrate on the morning trading session, this one is optimized for overnight execution, starting at 6:30 PM ET. Trading during this time typically offers more controlled movement and cleaner signals, making it ideal for algorithmic performance. We’re using a 30-minute chart, which provides a good balance between signal sensitivity and noise reduction. It allows the algorithm to identify meaningful moves while filtering out the short-term volatility often seen on smaller time frames. This charting approach supports the strategy’s goals of steady daily profits within a controlled risk framework.

This series is just one example of the many configurations possible within ITG Quantum AI. The algorithm was built with flexibility in mind, allowing traders to adapt it to different instruments, time frames, and volatility conditions. More importantly, it is designed to help traders achieve consistent monthly returns—an essential requirement for implementing the Compound Risk Reinvestment System (CRRS). Whether you’re trading with full automation or integrating the AI into a broader discretionary strategy, success ultimately depends on discipline, structure, and repeatable rules. Be sure to watch all four parts of the series and visit the Edge section of our site for more insights on how this strategy integrates into a long-term trading plan.