Overview

This strategy combines multiple technical indicators to achieve clear trend tracking. The main components are:

1. Trend judgement based on moving averages
2. Oversold/overbought analysis using stochastic oscillator
3. Funds flow analysis with price & volume indicators
4. Trend quality measurement using volatility index
5. Divergence detection with RSI

By synthesizing signals from these indicators, the strategy can identify trends more precisely. It will go long when golden cross happens and go short when dead cross appears.

Principle

Firstly, moving averages and their envelopes are used to determine the trend direction. Price breaking through the envelope may signal potential trend reversal.

Secondly, KD lines from the stochastic oscillator are used to detect oversold/overbought conditions, which usually imply opportunities for reversal.

Then, price-volume indicators are constructed to analyze the funds flow. Rising volume represents capital inflow and trend continuation, while falling volume indicates capital outflow and trend reversal.

To quantify trend quality, a volatility index is built from average price range, and its EMA measures the strength of the trend. This helps filter out fake trends.

Finally, divergences between price and RSI may also indicate upcoming trend reversals.

By combining all these signals, the trend can be identified more precisely. The strategy will go long when golden cross between MAs appears, and go short when dead cross happens.

Advantages

• Noise reduction and clearer signals using multiple indicators
• Oversold/overbought analysis provides good reversal timing
• Volume analysis prevents false breakouts
• Volatility index measures trend quality to avoid choppiness
• RSI divergence offers additional reversal signal
• Clean code structure, easy to understand and modify

Risks

• Signal conflicts may occur when combining multiple indicators, requiring careful parameter tuning
• Rising volume could also be manipulated, prudent judgement needed
• RSI parameters may need adjustment for different products
• Whipsaws and wrong signals often occur during ranging markets
• Indicator performance may degrade in inefficient markets

Risk management:

• Enhance parameter optimization for proper indicator behaviors
• Configure indicator weighting to resolve conflicts
• Adjust parameters based on product characteristics
• Increase position sizing to reduce excessive trading
• Verify performance via backtesting and paper trading

Optimizations

This strategy can be improved in the following aspects:

1. Use machine learning to auto-tune parameters for different products

2. Add model evaluation to dynamically adjust indicator weights based on market conditions

3. Implement adaptive stop loss based on market volatility

4. Incorporate deep learning for more accurate trend prediction

5. Build auto signal reconciliation to resolve conflicts and reduce false signals

6. Integrate more indicators for ensemble system prediction

7. Explore parameterless indicators to reduce parameter dependence

Conclusion

This strategy leverages multiple technical indicators to achieve relatively robust trend identification, with promising application potential. However, its accuracy and risk management need continuous improvements before stable live trading. Future optimizations may incorporate machine learning and other techniques to enable intelligent automation.