This paper introduces a technical trading framework that features trend-following, conditional active trading, stop-loss mechanisms and trading volume in formulating trading strategies. Unlike analyses focusing on a single indicator, this framework reflects a comprehensive approach, integrating multiple trading principles into a technical analysis. The implementation in this study mainly relies on common technical indicators such as moving average crossovers and moving average convergence/ divergence. Empirical results on data from 2007 to 2023 demonstrate that trading strategies derived from this framework outperforms the buy-and-hold approach on US index exchange-traded funds such as the SPDR S&P 500 ETF Trust and Invesco QQQ Trust (Series 1). Using a large, survivorship-bias-free US stock sample from 2000 to 2023, strategies selected through machine learning exhibit higher average returns and reduced drawdowns compared with buy and hold. An optimized multilayer perceptron neural network is employed to support strategy parameter selection. In addition, moving average gap volatility and downside price volatility prove valuable in parameter selection for the trading strategies.