Golden Cross Death Cross Strategy

Overview

This strategy uses the golden cross and death cross principles of simple moving averages to implement long and short positions for stocks. It goes long when the fast MA crosses above the slow MA, and goes short when the fast MA crosses below the slow MA.

Strategy Logic

The strategy first defines the backtesting timeframe, then sets the calculation parameters for the two moving averages, including MA type and period length.

The getMAType() function calculates the values of the two MAs. fastMA is the shorter period MA, and slowMA is the longer period MA.

The core logic:

• When fastMA crosses above slowMA, a long signal is triggered.

• When fastMA crosses below slowMA, a short signal is triggered.

Finally, during backtesting, take long position when seeing long signal, and take short position when seeing short signal.

Advantage Analysis

• Simple and clear strategy idea, easy to understand and implement.
• Uses widely applied MA crossover principles, suitable for most stock products.
• Customizable MA types and parameters, high adaptability.
• Modular strategy structure, clear functionality, easy to optimize.

Risk Analysis

• MA crossovers have some lag, may miss some trading opportunities.
• Cannot effectively filter whipsaw markets, prone to being trapped.
• Parameter optimization is not comprehensive enough, requires manual experience.
• Unable to effectively control per trade risk and losses.

Possible optimizations against the risks:

1. Add other technical indicators for trend identification.

2. Add stop loss to control per trade loss amount.

3. Add volume indicators to avoid whipsaw markets.

4. Build parameter optimization mechanisms to find optimal parameter sets automatically.

Optimization Directions

The strategy can be further optimized in the following aspects:

1. Add stop loss strategies like fixed stop loss points or trailing stop loss to control losses.

2. Add position sizing strategies like fixed or dynamic position sizing to control trading risks.

3. Add filters by combining with other technical indicators to identify trends and improve win rate.

4. Optimize parameters by methods like grid search and linear regression to find optimum values.

5. Expand entry strategies like breakout pullback, scale in orders to enrich trading tactics.

6. Add volume indicators to avoid whipsaw markets.

7. Expand products to stock indexes, forex, cryptocurrencies etc.

Summary

This strategy implements long/short stock selection based on MA crossover principles. The strategy idea is simple and clear, widely used, highly adaptable, and practically valuable. But it also has some lagging and whipsaw filtering issues. Future optimizations can focus on improving stop loss, parameter optimization, adding filters etc to make it more advantageous.