Intelligent Moving Average Crossover Strategy with Dynamic Profit/Loss Management System

Overview

This strategy is an intelligent trading system based on moving average crossover signals, combined with a dynamic profit/loss management mechanism. The core strategy uses the crossover of 7-period and 40-period Simple Moving Averages (SMA) to generate trading signals, while integrating a percentage-based stop-loss and take-profit control system for precise risk management.

Strategy Principles

The strategy operates based on the following core mechanisms: 1. Signal Generation: Trading signals are generated by observing the crossover between short-period (7-day) and long-period (40-day) moving averages. Buy signals are generated when the short-term MA crosses above the long-term MA, and sell signals when it crosses below. 2. Position Management: The system employs a single position mechanism, preventing multiple entries while a position is open to ensure effective capital utilization. 3. Risk Control: Integrates a dynamic stop-loss/take-profit system based on entry price. Stop-loss is set at 1% below entry price, and take-profit at 2% above, enabling quantified risk management for each trade.

Strategy Advantages

1. Signal Reliability: Effectively captures price trend changes by combining fast and slow moving averages.
2. Comprehensive Risk Management: Incorporates dynamic stop-loss/take-profit mechanisms for precise risk control of each trade.
3. Parameter Flexibility: All key parameters can be adjusted through the interface, including MA periods and profit/loss percentages.
4. Visualization: Clearly displays moving averages and profit/loss levels on the chart for real-time monitoring.

Strategy Risks

1. MA Lag: Moving averages are inherently lagging indicators, potentially causing delays in volatile markets.
2. Sideways Market Risk: May generate frequent false signals in range-bound markets.
3. Fixed Stop-Loss Risk: Percentage-based fixed stops may lack flexibility under certain market conditions.

Strategy Optimization Directions

1. Signal Filtering: Recommend incorporating trend filters, such as ADX, to identify trend strength.
2. Dynamic Stops: Consider linking stop-loss levels to market volatility for smarter risk management.
3. Position Sizing: Introduce volatility-based dynamic position sizing system.
4. Market Adaptability: Add market state recognition module for different parameter settings under various market conditions.

Summary

This strategy captures market trends through moving average crossovers while implementing risk management through dynamic profit/loss controls, demonstrating strong practicality. While there are inherent lag risks, the suggested optimization directions can further enhance strategy stability and profitability. The strategy’s high configurability makes it suitable for further refinement and customization.