Grid trading is a type of trading strategy that involves buying and selling financial instruments at predetermined levels, or "grid levels", in order to profit from price movements.

There are many other complex strategies that can be used for grid trading. Some other examples include:

• Using multiple timeframes to generate entry and exit signals. For example, using a daily chart to determine the overall trend and a shorter timeframe chart, such as a 15-minute chart, to generate entry and exit signals.
• Incorporating fundamental analysis to make decisions. For example, using financial ratios or economic indicators to determine if a stock is overvalued or undervalued.
• Using machine learning techniques to generate predictions, such as neural networks or support vector machines.
• Utilizing volatility-based grid size adjustments. For example, adjusting the grid size based on the standard deviation of the price movements.
• Incorporating multiple indicators, such as the Moving Average, Bollinger Bands, RSI, etc.
• Incorporating multiple financial assets to generate signals.
• Incorporating risk management techniques such as adjusting the grid size or grid levels based on the volatility of the market.

It's important to note that creating a complex strategy doesn't necessarily mean it will be more profitable. Complex strategies can be more difficult to understand and implement, and they may also be more prone to errors. It's important to backtest and evaluate the performance of any strategy before implementing it in live trading.

Also, a complex strategy may require more computational resources and may be more difficult to optimize and fine-tune. It's important to have a good understanding of the markets, and the strategy and indicators you are using. It's also important to have a good understanding of programming and Pine script before attempting to code a complex strategy.