Low quality vs 99.9% tick data backtest

Testing a trading EA (Expert Advisor) using low-quality tick data compared to high-quality, 99.9% tick data can result in significant differences in the evaluation and performance of the EA. Here are five key differences between the two approaches:

1. Data Accuracy and Completeness:
   • Low-Quality Tick Data: Low-quality tick data may have missing or inaccurate information due to gaps in the data feed, data compression, or other issues. This can lead to unreliable backtest results and a skewed understanding of the EA’s performance.
   • 99.9% Tick Data: High-quality tick data is more accurate and complete, providing a near-continuous stream of ticks. This ensures that the EA is tested under realistic market conditions, leading to more reliable performance assessments.
2. Order Execution Realism:
   • Low-Quality Tick Data: When using low-quality data, the EA’s order executions may not accurately reflect real market conditions. Slippage and order fill behavior may be poorly represented, affecting the assessment of the EA’s performance.
   • 99.9% Tick Data: With high-quality tick data, you can better simulate real-world order execution scenarios, including slippage, spread variations, and order queue dynamics. This provides a more realistic evaluation of the EA’s capabilities.
3. Strategy Robustness Testing:
   • Low-Quality Tick Data: Low-quality data may not capture extreme market conditions, flash crashes, or other unusual events effectively. This can lead to the EA performing well in backtests but poorly in real trading when faced with unexpected market events.
   • 99.9% Tick Data: Comprehensive tick data includes a wide range of market conditions, allowing you to test the EA’s robustness under various scenarios. It helps identify potential weaknesses and vulnerabilities that may not be apparent with limited data.
4. Accuracy of Performance Metrics:
   • Low-Quality Tick Data: Inaccurate data can result in misleading performance metrics, such as profit and drawdown calculations. This can lead to overestimation or underestimation of the EA’s true risk and reward characteristics.
   • 99.9% Tick Data: High-quality data provides more precise performance metrics, allowing you to assess the EA’s profitability, risk management, and drawdowns more accurately. This information is crucial for making informed trading decisions.
5. Backtest Sensitivity:
   • Low-Quality Tick Data: The sensitivity of backtest results to changes in input parameters or market conditions may be low with low-quality data. This can give a false sense of confidence in the EA’s performance.
   • 99.9% Tick Data: High-quality data allows you to conduct sensitivity analysis more effectively, helping you understand how the EA’s performance varies with different parameters or market conditions. This can lead to more robust trading strategies.

In summary, using 99.9% tick data for testing a trading EA is essential for obtaining more accurate and realistic performance evaluations, ensuring that the EA is better prepared to handle various market scenarios and reducing the risk of unexpected issues in live trading.

Check out my detailed live testing video: