Multi-Indicator Dynamic Stop-Loss Momentum Trend Trading Strategy

Published at

12/30/2024

Categories

4 categories in total

Author

8 person written this

fmzquant

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Multi-Indicator Dynamic Stop-Loss Momentum Trend Trading Strategy

Overview
This strategy is a comprehensive trading system that combines multiple technical indicators to capture trading opportunities by dynamically monitoring market momentum and trend changes. The strategy integrates multiple indicators including Moving Averages (EMA), Relative Strength Index (RSI), Moving Average Convergence Divergence (MACD), Bollinger Bands (BB), and implements a dynamic stop-loss mechanism based on Average True Range (ATR), achieving multi-dimensional market analysis and risk control.

Strategy Principle
The strategy employs a multi-level signal confirmation mechanism, including:

Trend Determination: Uses 7-period and 14-period EMA crossovers to determine market trend direction
Momentum Analysis: Monitors market overbought/oversold conditions using RSI with 30/70 dynamic thresholds
Trend Strength Confirmation: Incorporates ADX indicator to judge trend strength, confirming strong trends when ADX>25
Volatility Range Assessment: Utilizes Bollinger Bands to define price volatility ranges and generates trading signals
Volume Verification: Employs dynamic volume moving average filtering to ensure sufficient market activity
Risk Control: Dynamic stop-loss strategy based on ATR indicator, with stop-loss distance set at 1.5 times ATR

Strategy Advantages

Multi-dimensional signal verification reduces false signals
Dynamic stop-loss mechanism enhances risk adaptation capability
Combination of volume and trend strength analysis improves trading reliability
Adjustable indicator parameters provide good adaptability
Complete entry and exit mechanisms with clear trading logic
Uses standard technical indicators, easy to understand and maintain

Strategy Risks

Multiple indicators may lead to signal lag
Parameter optimization may result in overfitting
May generate frequent trades in ranging markets
Complex signal system might increase computational load
Requires large sample size for strategy validation

Strategy Optimization Directions

Introduce market volatility adaptive mechanism for dynamic parameter adjustment
Add time filters to avoid trading during unfavorable periods
Optimize profit-taking strategy, consider implementing trailing stops
Include transaction cost considerations in entry/exit conditions
Implement position management mechanism for dynamic position sizing

Summary
The strategy constructs a comprehensive trading system through the coordination of multiple indicators. Its core advantages lie in multi-dimensional signal confirmation and dynamic risk control systems, while attention needs to be paid to parameter optimization and market adaptability. Through continuous optimization and adjustment, the strategy has the potential to maintain stable performance across different market conditions.

Strategy source code

/*backtest
start: 2024-11-19 00:00:00
end: 2024-12-19 00:00:00
period: 1h
basePeriod: 1h
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=5
strategy("XRP/USDT Scalping Strategy", overlay=true)

// Input Parameters
emaShortLength = input.int(7, title="Short EMA Length")
emaLongLength = input.int(14, title="Long EMA Length")
rsiLength = input.int(7, title="RSI Length")
rsiOverbought = input.int(70, title="RSI Overbought Level") // Adjusted to 70 for broader range
rsiOversold = input.int(30, title="RSI Oversold Level") // Adjusted to 30 for broader range
macdFastLength = input.int(12, title="MACD Fast Length")
macdSlowLength = input.int(26, title="MACD Slow Length")
macdSignalLength = input.int(9, title="MACD Signal Length")
bbLength = input.int(20, title="Bollinger Bands Length")
bbStdDev = input.float(2.0, title="Bollinger Bands Standard Deviation") // Adjusted to 2.0 for better signal detection

// EMA Calculation
emaShort = ta.ema(close, emaShortLength)
emaLong = ta.ema(close, emaLongLength)

// RSI Calculation
rsi = ta.rsi(close, rsiLength)

// MACD Calculation
[macdLine, signalLine, _] = ta.macd(close, macdFastLength, macdSlowLength, macdSignalLength)
macdHistogram = macdLine - signalLine

// Bollinger Bands Calculation
basis = ta.sma(close, bbLength)
deviation = ta.stdev(close, bbLength)
bbUpper = basis + (bbStdDev * (deviation > 1e-5 ? deviation : 1e-5)) // Ensure robust Bollinger Band calculation
bbLower = basis - bbStdDev * deviation

// Volume Condition
volCondition = volume > ta.sma(volume, input.int(20, title="Volume SMA Period")) // Dynamic volume filter

// Trend Strength (ADX)
// True Range Calculation
tr = math.max(high - low, math.max(math.abs(high - close[1]), math.abs(low - close[1])))
// Directional Movement
plusDM = high - high[1] > low[1] - low ? math.max(high - high[1], 0) : 0
minusDM = low[1] - low > high - high[1] ? math.max(low[1] - low, 0) : 0
// Smooth Moving Averages
atr_custom = ta.rma(tr, 14)
plusDI = 100 * ta.rma(plusDM, 14) / atr_custom // Correct reference to atr_custom
minusDI = 100 * ta.rma(minusDM, 14) / atr_custom // Correct reference to atr_custom
// ADX Calculation
adx = plusDI + minusDI > 0 ? 100 * ta.rma(math.abs(plusDI - minusDI) / (plusDI + minusDI), 14) : na // Simplified ternary logic for ADX calculation // Prevent division by zero // Prevent division by zero // Final ADX
strongTrend = adx > 25

// Conditions for Buy Signal
emaBullish = emaShort > emaLong
rsiOversoldCondition = rsi < rsiOversold
macdBullishCrossover = ta.crossover(macdLine, signalLine)
priceAtLowerBB = close <= bbLower

buySignal = emaBullish and (rsiOversoldCondition or macdBullishCrossover or priceAtLowerBB) // Relaxed conditions by removing volCondition and strongTrend

// Conditions for Sell Signal
emaBearish = emaShort < emaLong
rsiOverboughtCondition = rsi > rsiOverbought
macdBearishCrossover = ta.crossunder(macdLine, signalLine)
priceAtUpperBB = close >= bbUpper

sellSignal = emaBearish and (rsiOverboughtCondition or macdBearishCrossover or priceAtUpperBB) // Relaxed conditions by removing volCondition and strongTrend

// Plot EMA Lines
trendColor = emaShort > emaLong ? color.green : color.red
plot(emaShort, color=trendColor, title="Short EMA (Trend)") // Simplified color logic
plot(emaLong, color=color.red, title="Long EMA")

// Plot Bollinger Bands
plot(bbUpper, color=color.blue, title="Upper BB")
plot(bbLower, color=color.blue, title="Lower BB")

// Plot Buy and Sell Signals
plot(emaBullish ? 1 : na, color=color.green, linewidth=1, title="Debug: EMA Bullish")
plot(emaBearish ? 1 : na, color=color.red, linewidth=1, title="Debug: EMA Bearish")
plot(rsiOversoldCondition ? 1 : na, color=color.orange, linewidth=1, title="Debug: RSI Oversold")
plot(rsiOverboughtCondition ? 1 : na, color=color.purple, linewidth=1, title="Debug: RSI Overbought")
plotshape(series=buySignal, title="Buy Signal", location=location.belowbar, color=color.green, style=shape.labelup, text="BUY")
plotshape(series=sellSignal, title="Sell Signal", location=location.abovebar, color=color.red, style=shape.labeldown, text="SELL", size=size.small) // Dynamic size for signals

// Strategy Execution with ATR-based Stop Loss and Take Profit
// Reuse atr_custom from earlier calculation
stopLoss = low - (input.float(1.5, title="Stop Loss Multiplier") * atr_custom) // Consider dynamic adjustment based on market conditions // Adjustable stop-loss multiplier
takeProfit = close + (2 * atr_custom)

if (buySignal)
    strategy.entry("Buy", strategy.long, stop=stopLoss) // Removed limit to simplify trade execution

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

Strategy parameters

The original address: https://www.fmz.com/strategy/475621

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Multi-Indicator Dynamic Stop-Loss Momentum Trend Trading Strategy