This study investigates the potential of replacing the Average True Range (ATR) with a predictive True Range (TR) estimate to enhance the perfor-mance of the Opening Range Breakout (ORB) trading strategy. An initial or-acle-based backtest demonstrates that access to future TR values significant-ly improves cumulative returns. Building on this insight, a Long Short-Term Memory (LSTM) neural network is employed to forecast next-day TR using a sliding window framework, focusing specifically on S&P 500 Index Futures (ES). The results indicate that incorporating the predicted TR into the ORB strategy substantially improves performance, achieving a cumulative return gain of approximately 70% relative to the ATR-based baseline. Forecasting accuracy is assessed using the Mean Absolute Percentage Error (MAPE), demonstrating consistent predictive capability. These findings provide empir-ical evidence that machine learning-based volatility forecasting can mitigate the lag inherent in traditional indicators, supporting more adaptive and data-driven intraday trading strategies.