Core Insights - The paper presents a technical trading framework that incorporates trend-following, conditional active trading, stop-loss mechanisms, and trading volume to formulate trading strategies [1] - This framework integrates multiple trading principles rather than relying on a single indicator, providing a comprehensive approach to technical analysis [1] - Empirical results from 2007 to 2023 indicate that strategies derived from this framework outperform the buy-and-hold strategy for US index exchange-traded funds like SPDR S&P 500 ETF Trust and Invesco QQQ Trust (Series 1) [1] Trading Strategies - The study utilizes common technical indicators such as moving average crossovers and moving average convergence/divergence [1] - Machine learning techniques applied to a large, survivorship-bias-free US stock sample from 2000 to 2023 show that selected strategies yield higher average returns and lower drawdowns compared to the buy-and-hold approach [1] - An optimized multilayer perceptron neural network is used for strategy parameter selection, enhancing the effectiveness of the trading strategies [1] Volatility Analysis - Moving average gap volatility and downside price volatility are identified as valuable factors in the parameter selection process for the trading strategies [1]