SAR Alternating Timeframe Trading Strategy

Overview

This strategy is based on the alternating operations of the SAR indicator across different timeframes. The strategy calculates the SAR indicator in 15-minute, daily, weekly and monthly timeframes, and trades in the weekly timeframe. It goes long when the weekly SAR crosses above the highest price and goes short when crossing below the lowest price.

Principles

SAR Indicator Calculation

The Parabolic SAR (SAR) indicator represents parabolic SAR, which judges the trend direction by calculating the relationship between the current price and historical prices. When the price breaks through the SAR point, it indicates a trend reversal.

This strategy calculates SAR values in 15-minute, daily, weekly and monthly timeframes, respectively. The formula is:

SAR = Previous SAR + Acceleration Factor * (Highest Price - Previous SAR) # Uptrend 
SAR = Previous SAR + Acceleration Factor * (Lowest Price - Previous SAR) # Downtrend

The initial acceleration factor is set at 0.02, and will gradually increase up to a maximum of 0.2 as the trend extends.

Trading Strategy

The strategy generates trading signals in the weekly timeframe. It goes long when the weekly SAR crosses above the highest price, with SAR value as the stop loss. It goes short when SAR crosses below the lowest price, with SAR as the stop loss.

By determining the trend on a higher timeframe and setting a more precise stop loss level, the strategy aims to profit more efficiently.

Advantages

• SAR indicator accurately locates trend reversal points for entering the market
• Trading in higher timeframes follows the major trend
• Stop loss stuck to SAR effectively controls risks

Risks & Solutions

• SAR lag may cause trend to reverse after hitting stop loss. Solution is to allow wider stop loss distance.
• Acceleration factor may expand dramatically on huge trends, causing stop loss to be penetrated. Solution is to limit maximum factor size.
• Long drawdown periods during long cycles on higher timeframes. Solution is to reduce position sizes.

Improvement Areas

• Optimize entry conditions, e.g. combine with other indicators
• Enhance stop loss mechanisms, e.g. trailing stop loss, zone stop loss
• Refine position sizing rules, e.g. fixed fractional, dynamic adjustment
• Operate on even higher timeframes, e.g. quarterly, annually
• Dynamically optimize parameters via machine learning

Conclusion

The strategy has clear logic of riding trends on higher timeframes using SAR indicator to locate reversals and set stop loss. Entry signals and risk management could be further improved. With optimizations in areas like entries, stops and position sizing, it can become more stable and profitable.