Good back testing techniques?

<p id="isPasted">I just use these:</p><p>Fully automated – TradeStation</p><p>Semi-automated / manual – Forex Tester Online</p>

<p id="isPasted">Good backtesting techniques are used to evaluate and validate a trading strategy's performance using historical data while avoiding common biases and pitfalls. The best techniques involve rigorous methodology and advanced validation methods beyond a simple backtest to ensure the strategy is robust and not merely overfitted to past data.&nbsp;</p><p><strong>Advanced techniques</strong></p><p><em>Walk-forward optimization</em>: Instead of testing a strategy on a single block of historical data, this method divides the data into multiple "in-sample" and "out-of-sample" periods.</p><ul><li>First, you optimize the strategy's parameters on the initial in-sample data.</li><li>Next, you test its performance on the following, out-of-sample data.</li><li>This process is repeated by "walking forward" through the dataset, which helps test the strategy's adaptability to changing market conditions and prevents overfitting.</li></ul><p><em>Out-of-sample testing</em>: This is a crucial technique for validating a backtested strategy. You develop and optimize your strategy on one portion of the data (the "in-sample" data) and then test it on a separate, unseen portion of data (the "out-of-sample" data). If the strategy performs well on the unseen data, it is more likely to be robust in live trading.</p><p><em>Randomized out-of-sample testing</em>: To overcome the limitations of selecting a single out-of-sample period, you can create multiple, non-congruent random out-of-sample periods from your dataset. The strategy is tested on each of these random datasets to ensure consistent performance, confirming its resilience beyond a single market environment.</p><p><em>Monte Carlo simulations</em>: This technique generates thousands of random outcomes for market variables (e.g., price, volatility) to create a wide range of possible future paths. By simulating how a strategy might perform under diverse and unpredictable conditions, it helps evaluate robustness more thoroughly than a traditional, fixed chronological backtest.</p><p><em>Noise tests</em>: This advanced validation method, available on some platforms like Build Alpha, adds varying amounts of "noise" or volatility to historical data and then re-tests the strategy. An overfitted strategy will fail this test because it was optimized to the noise of the original data, whereas a robust strategy should continue to perform well on the adjusted price series.</p><p><em>Portfolio backtesting</em>: For traders managing multiple assets or strategies, this technique backtests the performance of the entire portfolio, not just a single instrument. This helps identify the optimal asset allocation to achieve desired objectives and manage risk.&nbsp;</p>