Overview
This strategy is a trading system based on the crossover of exponential moving average (EMA), which realizes dynamic risk management by combining the average true range (ATR). The strategy uses two EMA lines, short-term and long-term, to capture the momentum changes of price trends, and uses ATR to dynamically set the stop-profit and stop-loss positions, thus achieving precise control of trading risks.

Strategy Principle
The core logic of the strategy is based on the crossover signals of two exponential moving averages of different periods (9 and 21). When the short-term EMA crosses the long-term EMA upward, a long signal is generated; when the short-term EMA crosses the long-term EMA downward, a short signal is generated. In order to better manage risks, the strategy introduces a dynamic stop-profit and stop-loss mechanism based on the 14-period ATR. The take-profit level is set to 2 times ATR, and the stop-loss level is set to 1 times ATR. This setting not only ensures sufficient profit space, but also can control risks in a timely manner.

Strategy Advantages

1. Dynamic risk management: Dynamically adjust the take-profit and stop-loss positions through ATR, so that the strategy can better adapt to changes in market volatility.
2. Trend tracking ability: The EMA crossover system can effectively capture medium- and long-term trends and reduce false signals.
3. Optimization of risk-return ratio: The take-profit distance is twice the stop-loss distance, which complies with the principle of a good risk-return ratio.
4. Strong adaptability: Strategy parameters can be adjusted according to different market conditions and have strong adaptability.

Strategy Risks

1. Risk of volatile market: In a sideways and volatile market, frequent false breakout signals may occur, leading to continuous stop losses.
2. Slippage risk: When the market fluctuates violently, the actual transaction price may deviate significantly from the price when the signal is generated.
3. Parameter sensitivity: The choice of EMA period has an important impact on strategy performance, and different market environments may require different parameter settings.

Strategy Optimization Direction

1. Introduce trend filters: You can add longer-period moving averages or ADX indicators to filter trend strength and only trade in strong trend environments.
2. Optimize position management: You can dynamically adjust the position size according to the ATR value and reduce the position when volatility is high.
3. Add time filter: You can add trading time filter to avoid trading during periods of poor market liquidity.

Summary
This strategy realizes a relatively complete trading system by combining the classic EMA crossover system and dynamic ATR risk management. The main advantages of the strategy are its dynamic risk management capabilities and good trend tracking characteristics. Through the recommended optimization direction, the strategy has room for further improvement. When applying it in real market, it is recommended to conduct sufficient backtesting and parameter optimization, and make appropriate adjustments according to specific market characteristics.

Strategy source code

/*backtest
start: 2019-12-23 08:00:00
end: 2025-01-04 08:00:00
period: 1d
basePeriod: 1d
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=5  
strategy("Improved EMA Crossover Strategy", overlay=true)  

// User-defined inputs for EMAs  
shortTermLength = input(9, title="Short-Term EMA Length")  
longTermLength = input(21, title="Long-Term EMA Length")  


// Dynamic Take Profit and Stop Loss  
atrLength = input(14, title="ATR Length")  
atrMultiplierTP = input(2.0, title="ATR Multiplier for Take Profit")  
atrMultiplierSL = input(1.0, title="ATR Multiplier for Stop Loss")  

// Calculate EMAs and ATR  
shortTermEMA = ta.ema(close, shortTermLength)  
longTermEMA = ta.ema(close, longTermLength)  
atr = ta.atr(atrLength)  

// Plot the EMAs  
plot(shortTermEMA, color=color.blue, title="Short-Term EMA")  
plot(longTermEMA, color=color.red, title="Long-Term EMA")  

// Generate Entry Conditions  
longCondition = ta.crossover(shortTermEMA, longTermEMA)  
shortCondition = ta.crossunder(shortTermEMA, longTermEMA)  

// Optional Debugging: Print conditions (you can remove this later)  
var label longLabel = na  
var label shortLabel = na  
if longCondition  
    longLabel := label.new(bar_index, high, "Buy Signal", color=color.green, style=label.style_label_down, textcolor=color.white)  
if shortCondition  
    shortLabel := label.new(bar_index, low, "Sell Signal", color=color.red, style=label.style_label_up, textcolor=color.white)  

if (longCondition)  
    strategy.entry("Long", strategy.long)  
    strategy.exit("Long Exit", "Long", limit=close + atr * atrMultiplierTP, stop=close - atr * atrMultiplierSL)  

if (shortCondition)  
    strategy.entry("Short", strategy.short)  
    strategy.exit("Short Exit", "Short", limit=close - atr * atrMultiplierTP, stop=close + atr * atrMultiplierSL)

Strategy Parameters

The original address: Dynamic ATR-Adjusted EMA Crossover Strategy