Whether grid trading strategies require Tick-level data for backtesting actually depends on a comprehensive consideration of multiple factors. First, we need to clarify the characteristics of Tick data and grid trading strategies.
Tick data, as the most detailed trading data structure in the market, indeed contains rich information such as the price and quantity of orders on the order book and real-time transaction data. This information is crucial for backtesting high-frequency quantitative trading strategies, as high-frequency strategies need to make decisions in extremely short time frames and rely on these subtle price movements.
However, grid trading strategies typically fall into the category of low-frequency trading strategies. Their core idea is to automatically buy and sell based on preset grids to capture small market fluctuations. Such strategies do not rely on rapid decision-making, therefore, they do not require the real-time nature of Tick-level data.
Additionally, backtesting with Tick data poses some challenges. Tick data is complex, slow to process, and requires a long period, which can increase the difficulty and time cost of backtesting. In contrast, using 1-minute K-line data for backtesting can greatly simplify the process and improve efficiency.
Taking the aijiebot cryptocurrency quantitative trading system as an example, this system employs 1-minute K-line data for backtesting grid trading strategies and achieves results close to real trading. This fully demonstrates that using 1-minute K-line data is sufficient for backtesting grid trading strategies.
In summary, for grid trading strategies, using Tick-level data for backtesting is not necessary. Instead, using 1-minute K-line data can adequately simulate the actual trading environment and provide strong support for strategy optimization and evaluation.