Quantitative Trading Strategy Based on Double EMA Crossover

Overview

This strategy generates trading signals by calculating the crossover between two EMA lines of different periods to determine market trends. It will open long positions when the shorter period EMA crosses over the longer period EMA, indicating an uptrend, and it will close positions when the shorter period EMA crosses below the longer period EMA, indicating a downtrend.

Principles

The strategy mainly applies the golden cross and death cross theory of double EMA lines. The double EMA lines consist of a long EMA and a short EMA. The short EMA parameter is set to 10 days and the long EMA parameter is set to 21 days.

When the short EMA crosses over the long EMA, a buy signal is generated. When the short EMA crosses below the long EMA, a sell signal is generated. The strategy also sets growth rate thresholds, opening long positions only when growth exceeds a positive threshold and closing positions only when decline exceeds a negative threshold.

Specifically, the buy condition is when the short EMA is higher than the long EMA and the stock growth rate exceeds the positive threshold. The close position condition is when the short EMA is lower than the long EMA and the stock growth rate falls below the negative threshold.

Advantages

• Utilizes the golden cross and death cross theory of double EMA lines for simplicity and reliability
• Adds growth rate thresholds to avoid erroneous trades during weak growth
• Can strictly control maximum loss ratio
• EMA period parameters can be flexibly adjusted for different cycles

Risk Analysis

• EMA lines have lagging effects, possibly missing price reversal points
• Line crossovers have some lag, possibly missing best entry points
• Relies on parameter optimization, improper settings may cause overtrading or insufficient signals

Optimization Directions

• Combine with other indicators like MACD, KD etc. to improve signal accuracy
• Add stop loss strategies like trailing stop loss to maximize profits
• Optimize EMA period parameters for best settings across different products
• Incorporate real-time data and machine learning methods for dynamic parameter adjustment and optimization

Summary

The overall strategy is relatively simple and reliable, using double EMA crossovers to determine price trends and setting growth rate thresholds to generate trading signals. Compared to single line crossovers, it can filter out some false signals. But EMA lines themselves have lagging issues. Combining other indicators or dynamic parameter adjustment can further improve strategy performance.