Most people believe that quantitative trading is about finding the parameters with the highest historical returns. However, truly professional strategy systems never pursue a beautiful backtesting curve. Instead, they first ask a more brutal question: Can this set of parameters survive outside the training set? QuantDinger's strategy parameter tuning already supports IS/OOS sample splitting: The first 70% is used to train the parameters, and the last 30% is used to verify generalization ability. It also supports various optimization methods such as grid search, random search, differential evolution, Bayesian/TPE, etc. This means that the strategy is no longer just about "making money in backtesting," but has entered a true engineering verification process: Training → Optimization → Out-of-sample validation → Overfitting identification → Then consider live trading. The barrier to entry in quantitative trading shouldn't be limited to knowing how to write a few indicators. The real barrier is whether you can transform a strategy idea into a verifiable, reproducible, and iterative system. This is also what QuantDinger aims to do. github.com/brokermr810/Quant… #QuantDinger #OpenSourceQuantitativeAnalysis #AIQuantitativeAnalysis #QuantitativeTrading #StrategyBacktesting

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