Overview

This strategy identifies strong trends and favorable timing for short-term trading with loss control. It tracks price breakouts of simple moving averages as trend signals and sets stop loss/take profit based on RSI divergences to capture short-term price movements.

Strategy Logic

1. Calculating multi-period simple moving averages

   • Setting 9-day, 50-day and 100-day SMAs

   • Short SMA crossing long SMA indicates trend direction

2. Judging overbought/oversold levels using RSI

   • RSI length is 14 periods

   • RSI above 70 is overbought, below 30 is oversold

3. Entering trades when price breaks 9-day SMA

   • Go long when price breaks above 9-day SMA

   • Go short when price breaks below 9-day SMA

4. Setting stop loss/take profit based on RSI divergences

   • RSI divergence for stop loss

   • Take profit when RSI reaches preset levels

Advantage Analysis

• Captures short-term trends, suitable for high frequency trading

• SMA combos filter trend signals, avoiding bad trades

• RSI helps determine timing, effectively control risks

• Flexible stop loss/take profit locks short-term profits

• Combining indicators improves stability

Risk Analysis

• Inaccurate short-term trend judgment causes chasing

• False RSI signals expand losses

• Improper stop loss/take profit settings reduce profit or magnify losses

• High trading frequency increases costs and slippage

• Ineffective parameters and abnormal markets impact strategy

• Optimize parameters, strict stop loss, manage costs

Optimization Directions

• Test different SMA combos to improve trend judgment

• Consider additional indicators like STOCH to verify RSI signals

• Employ machine learning to determine valid breakouts

• Adjust parameters for different products and sessions

• Optimize stop loss/take profit logic for dynamic trailing

• Explore auto parameter tuning mechanisms

Conclusion

This strategy combines SMA and RSI for a conservative short-term trading approach. Fine-tuning parameters, validating signals, controlling risks makes it more robust and adaptive. There is room for improvement by exploring more SMA combos, adding machine learning models etc. Continuous optimization will lead to further maturity.