Overview
This strategy is a quantitative trading system based on the crossover of the double exponential moving average (EMA). It uses the crossover of the short-term EMA (14 periods) and the long-term EMA (100 periods) to capture the turning point of the market trend, and determines the entry time by judging the crossover position of the short-term moving average and the long-term moving average. When the short-term EMA crosses the long-term EMA upward, a buy signal is generated, and vice versa, a sell signal is generated. This strategy is particularly suitable for traders who want to make a layout at the beginning of a trend reversal.

Strategy Principle
The core logic of the strategy is based on the change in the momentum of the price trend. The short-term EMA is more sensitive to price changes, while the long-term EMA can better filter market noise and reflect the main trend. When the short-term moving average crosses the long-term moving average, it indicates that the short-term price momentum is increasing and the market may start to enter an upward trend; when the short-term moving average crosses the long-term moving average, it indicates that the short-term momentum is weakening and the market may turn to a downward trend. The strategy uses the ta.crossover and ta.crossunder functions to accurately capture these intersections and perform position operations at the appropriate time.

Strategy Advantages

1. The operation logic is clear and simple, easy to understand and execute
2. Can effectively capture the starting point of the trend and grasp the main market conditions
3. Have good risk control ability, automatically stop loss through moving average crossover
4. By using the dynamic characteristics of EMA, you can respond to price changes more quickly.
5. Supports custom parameter adjustment and can be optimized according to different market characteristics
6. Possessing automated execution capabilities to reduce human emotional interference

Strategy Risks

1. Frequent false signals may occur in volatile markets
2. The moving average crossover has a certain lag, and you may miss the best entry point
3. Large drawdowns may occur in fast-moving markets
4. Improper parameter selection may lead to signal quality degradation
5. Need to consider the impact of transaction costs on strategy returns

Strategy Optimization Direction

1. Introducing volume indicators as auxiliary confirmation signals
2. Add trend strength filter to reduce false breakout risk
3. Optimize the moving average period parameters to make them more suitable for specific markets
4. Add dynamic stop loss mechanism to improve risk control capabilities
5. Combined with other technical indicators to improve signal reliability
6. Develop adaptive parameter mechanisms to improve strategy adaptability

Summary
EMA trend crossover dynamic entry quantitative strategy is a classic and practical trend tracking system. By combining short-term and long-term exponential moving averages, the strategy can better grasp the market trend conversion opportunities. Although there is a certain lag and false signal risk, stable trading results can still be achieved through appropriate parameter optimization and risk control measures. The simplicity and scalability of the strategy make it a good basic framework for quantitative trading.

Strategy source code

/*backtest
start: 2019-12-23 08:00:00
end: 2024-12-11 08:00:00
period: 1d
basePeriod: 1d
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

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

// Input for EMAs
shortEmaLength = input(14, title="Short EMA Length")
longEmaLength = input(100, title="Long EMA Length")

// Calculate EMAs
shortEma = ta.ema(close, shortEmaLength)
longEma = ta.ema(close, longEmaLength)

// Plot EMAs
plot(shortEma, color=color.blue, title="9 EMA")
plot(longEma, color=color.red, title="100 EMA")

// Historical Signal Tracking
var float lastBuyPrice = na
var float lastSellPrice = na

// Buy and Sell Signals
buySignal = ta.crossover(shortEma, longEma)
sellSignal = ta.crossunder(shortEma, longEma)

// Track last buy and sell prices
if (buySignal)
    lastBuyPrice := close

if (sellSignal)
    lastSellPrice := close

// Plot buy and sell signals on the chart
plotshape(buySignal, title="Buy Signal", location=location.belowbar, color=color.green, style=shape.labelup, text="BUY")
plotshape(sellSignal, title="Sell Signal", location=location.abovebar, color=color.red, style=shape.labeldown, text="SELL")

// Strategy Logic
if (buySignal)
    strategy.entry("Buy", strategy.long)

if (sellSignal)
    strategy.close("Buy")

Strategy parameters

The original address: https://www.fmz.com/strategy/474967