The values of certain hyper-parameters can be optimized in order to improve the strategy:

• Asset used (BTC, ETH, MATIC, AVAX, etc.);
• Time frame used(5 min, 15 min, 1h, 4h,…);
• Values of Stop Loss and Take Profit;
• Parameters et periods of technical indicators;

…

Different combinations of settings can be tested to find the configuration that produces the best results. Analyzing and optimizing historical results can help better understand a strategy.

In addition, we can assess the robustness of a strategy by testing it on several different assets.

Backtesting a strategy is therefore necessary to assess the relevance of a strategy. However, it also has limitations. Indeed, although testing a strategy on historical data can provide useful information about its quality, it is important to note that neither the past nor the present guarantees the future. Financial markets can move quickly and unpredictably, which can cause a strategy to suffer significant losses. Additionally, simulated results may not fully account for other real-world factors that may impact business performance.

Alongside backtesting, careful development of a trading strategy often also involves incorporating real-time analysis as an evaluation tool. Live testing aims to assess the performance of a strategy in real-world market conditions in real time, where factors such as trading costs, slippage and liquidity can significantly affect its performance.

Backtesting and forward testing are two sides of the same coin, because both approaches aim to validate the effectiveness of a strategy and identify its strengths and weaknesses.