Parameter Optimized Trend Following Quantitative Strategy

Overview

The main idea of this strategy is to judge and track price trends by combining the percentrank indicator and parameter optimization. The strategy generates trading signals by comparing the current price with the percentage of prices over a certain historical period to capture the mirror effect and track trends for excess returns.

Strategy Principle

The strategy uses the percentrank indicator to determine price trends. Percentrank represents the relative strength of the current price over the viewed period. The parameter len indicates the length of the historical period to view.

The range of percentrank values is from 0 to 100. When the percentrank value is close to 0, it means the current price is near the lowest price in the viewed period and is in an undervalued area. When it is close to 100, it means the current price is near the highest price in the viewed period and is in an overvalued area.

The strategy also introduces a scale parameter as an offset to move the 0 to 100 range to the scale to 100+scale range. Two signal lines level_1 and level_2 are also set, where level_1 indicates the long level and level_2 indicates the short level.

When the price percentrank indicator crosses level_1 upwards, a long signal is generated. When it crosses level_2 downwards, a short signal is generated. The exit conditions are opposite of the entry signals.

Advantages of the Strategy

1. Use percentrank indicator to determine the strength of price trends, avoiding being trapped or chasing highs
2. Apply parameter optimization methods to adjust offset scale and signal line threshold for different products and cycles to improve stability
3. Combine trend following and mean reversion trading ideas to track trends in a timely manner after breaking through the signal line

Risk Analysis

1. Incorrect judgment of trends resulting in unnecessary losses
2. Prone to generating wrong signals when price volatility and trend are unclear
3. Improper parameter settings may lead to too frequent trading or insufficient trading volume

To address the above risks, parameters like len, scale, level can be adjusted for optimization. Other indicators can also be incorporated for confirmation to avoid erroneous trades.

Optimization Directions

There is room for further optimization of the strategy:

1. Stop loss points can be introduced to reduce single trade loss
2. Indicators like moving average can be incorporated for confirmation to filter out some wrong signals
3. Machine learning methods can be used to automatically optimize parameters
4. Can run in parallel across multiple time frames

Conclusion

The overall idea of the strategy is clear, applying quantitative methods of parameter optimization to judge and track price trends. It has some practical value but still needs further testing and optimization to reduce risks and improve stable profitability.