Multi-Indicator Momentum Dynamic Trailing Quantitative Trading Strategy

Overview
The Multi-Indicator Momentum Dynamic Trailing Quantitative Trading Strategy is an advanced trading system that combines multiple technical indicators designed to capture momentum opportunities within market trends. This strategy cleverly integrates trend filters (EMA crossovers), momentum identification (RSI), volume confirmation, MACD signals, and volatility analysis (Bollinger Band width) to construct a comprehensive trading decision framework. Additionally, the strategy employs an ATR-based risk management system, including flexible stop-loss settings, dynamic profit targets, and adaptive trailing stop functionality, aimed at optimizing the risk-reward ratio for each trade.

Strategy Principles
The core concept of this strategy is to identify key moments of price momentum change for entry, based on confirmed trend direction. Specifically, the strategy operates through the following mechanisms:

1. Trend Identification System: Uses the crossover of 20-period and 50-period Exponential Moving Averages (EMA) to determine the overall market trend direction. When EMA20 is above EMA50, an uptrend is identified; conversely, a downtrend is recognized.

2. Momentum Confirmation Mechanism: Captures price momentum through the 14-period Relative Strength Index (RSI). The strategy particularly focuses on RSI signals in the 40-60 range, which is viewed as a critical zone for momentum transitions. In an uptrend, RSI entering this zone is considered a bullish momentum signal; in a downtrend, the same range is interpreted as a bearish momentum signal.

3. Volume Verification: Requires current trading volume to exceed the 20-period average volume, ensuring sufficient market participation to support price movements.

4. MACD Filter (optional): When enabled, requires the relationship between the MACD line and signal line to align with the trading direction, further confirming trend momentum.

5. Volatility Assessment (optional): Compares Bollinger Band width with its 20-period average to ensure market volatility is sufficient to support trading signals.

6. Risk Management System:

• Uses Average True Range (ATR) to set dynamic stop-loss positions, defaulting to 1.5 times ATR
• Sets profit targets at 2.5 times the stop-loss distance, achieving a positive risk-reward ratio
• Offers trailing stop options to help lock in profits and allow trends to develop
• Establishes minimum holding periods to avoid premature exits from potentially profitable trades

When all these conditions are simultaneously met, the strategy generates entry signals and manages trades according to preset risk management parameters.

Strategy Advantages

1. Comprehensive Market Analysis Framework: By combining multiple technical indicators (EMA, RSI, MACD, Bollinger Bands), the strategy can evaluate market conditions from different angles, significantly improving signal quality.

2. Highly Adaptive Risk Management: Dynamic stop-losses and profit targets based on ATR allow the strategy to automatically adapt to different market volatility conditions without manual adjustment of fixed levels.

3. Flexible Parameterized Design: The strategy offers multiple adjustable parameters, such as risk-reward ratio, ATR multiplier, filter toggles, etc., enabling users to customize according to personal risk preferences and market conditions.

4. Combination of Momentum Trading and Trend Following: By identifying momentum change points within established trends, the strategy can capture the majority of trend profits while avoiding drawdowns when trends exhaust.

5. Multi-layer Filtering Mechanism: Various optional filters (MACD, Bollinger Band width) allow the strategy to adjust its sensitivity in different market environments, balancing trading frequency with signal quality.

6. Trailing Stop Functionality: When enabled, allows profits to continue growing without prematurely exiting profitable trades, while still providing downside protection.

Strategy Risks

1. False Signals Due to Signal Overlap: When multiple indicators are used for filtering simultaneously, trading signals may become overly diluted, missing favorable trading opportunities. To mitigate this risk, consider dynamically enabling or disabling certain filters based on market conditions.

2. Parameter Sensitivity: Multiple adjustable parameters (such as ATR multiplier, risk-reward ratio) significantly impact strategy performance; improper settings may lead to stops that are either too tight or too loose. Comprehensive backtesting is recommended to find optimal parameter combinations.

3. Trend Reversal Risk: Trend judgment relying on EMA crossovers may lag during initial trend reversals, resulting in losses during trend transitions. Consider adding more sensitive trend reversal indicators as supplements.

4. Limitations of Fixed Risk-Reward Settings: Although the strategy uses a fixed risk-reward ratio (default 2.5), different market environments may support different profit potentials. Consider dynamically adjusting the risk-reward ratio based on market volatility conditions.

5. Dual Nature of Minimum Holding Time: While minimum holding requirements help avoid premature exits, they may increase losses in rapidly reversing markets. Consider adjusting this parameter based on market speed and volatility.

Strategy Optimization Directions

1. Dynamic Parameter Adjustment Mechanism: Develop a mechanism to automatically adjust ATR multipliers, risk-reward ratios, and minimum holding times based on market volatility or trend strength. For example, increase the ATR multiplier in highly volatile markets to avoid being triggered by normal market noise.

2. Enhanced Trend Recognition System: The current EMA crossover method can be enhanced by adding trend strength indicators (such as ADX) or price structure analysis (such as higher highs/lower lows identification) to improve trend recognition accuracy.

3. Implementation of Time Filters: Consider adding time filters based on intraday timing, market volume patterns, or specific economic events to avoid trading during periods of abnormal volatility or high market uncertainty.

4. Dynamic Take-Profit Mechanism: The current fixed risk-reward ratio can be upgraded to a dynamic target-setting system based on support/resistance levels, price structure, or volatility expectations.

5. Correlated Market Synergy Signals: Integrate data from related markets (such as VIX, bond yields, or related industry ETFs) as additional confirmation layers to improve signal quality.

6. Machine Learning Optimization: Use machine learning algorithms to optimize strategy parameter combinations or develop a system to predict which parameter combination might perform best in the current market environment.

Summary
The Multi-Indicator Momentum Dynamic Trailing Quantitative Trading Strategy is a comprehensive, flexible, and adaptive trading system that captures market momentum opportunities while providing robust risk management capabilities through the fusion of trend recognition, momentum confirmation, and multi-layer filters. This strategy is particularly suitable for traders seeking to balance trading frequency and signal quality, as well as investors looking to identify high-probability entry points within confirmed trends.

By leveraging EMA trend confirmation, RSI momentum zone identification, volume verification, and optional MACD and Bollinger Band filters, the strategy can identify high-quality trading opportunities in the market. Meanwhile, its ATR-based stop-loss system, flexible risk-reward settings, and trailing stop functionality provide a comprehensive risk control framework for each trade.

Although the strategy is already a fully functional trading system, its performance can be further enhanced through the implementation of suggested optimization directions, such as dynamic parameter adjustments, enhanced trend recognition, and machine learning applications. For investors seeking to build systematic trading methods based on technical analysis, this strategy provides a solid foundation.

Strategy source code

/*backtest
start: 2025-05-01 00:00:00
end: 2025-07-05 10:00:00
period: 4h
basePeriod: 4h
exchanges: [{"eid":"Futures_Binance","currency":"ETH_USDT"}]
*/

//@version=6
strategy("NASDAQ Smart Momentum Strategy v4.1 Boosted", overlay=true, default_qty_type=strategy.percent_of_equity, default_qty_value=1, calc_on_order_fills=true, calc_on_every_tick=true)

// === Inputs ===
riskReward = input.float(2.5, "Risk-reward ratio", minval=1.0)
atrMult = input.float(1.5, "ATR multiplier for SL", minval=0.5)
useMACD = input.bool(true, "Activate MACD filter")
useBollFilter = input.bool(true, "Bollinger Band Width Activate Filter")
useTrailing = input.bool(true, "Activate Trailing Stop")
trailOffset = input.float(1.0, "Trailing-Offset ATR", minval=0.1)

// === Trendfilter: EMA20 & EMA50 Cross ===
ema20 = ta.ema(close, 20)
ema50 = ta.ema(close, 50)
plot(ema20, "EMA 20", color=color.blue)
plot(ema50, "EMA 50", color=color.orange)

trendUp = ema20 > ema50
trendDown = ema20 < ema50

// === RSI Momentum Range ===
rsi = ta.rsi(close, 14)
rsiMomentumLong = rsi > 40 and rsi < 60 and trendUp
rsiMomentumShort = rsi < 60 and rsi > 40 and trendDown

// === Volumenfilter ===
avgVolume = ta.sma(volume, 20)
volumeOK = volume > avgVolume

// === MACD Filter ===
[macdLine, signalLine, _] = ta.macd(close, 12, 26, 9)
macdBull = macdLine > signalLine
macdBear = macdLine < signalLine

// === Bollinger Band Width Filter ===
basis = ta.sma(close, 20)
dev = ta.stdev(close, 20)
bbUpper = basis + 2 * dev
bbLower = basis - 2 * dev
bbWidth = bbUpper - bbLower
avgBBWidth = ta.sma(bbWidth, 20)
bollRangeOK = bbWidth > avgBBWidth

// === ATR & TP/SL ===
atr = ta.atr(14)
slDist = atr * atrMult
tp = slDist * riskReward
trailDist = atr * trailOffset

// === Entry signals: combination of trend, RSI, volume, MACD, Bollinger ===
longSignal = rsiMomentumLong and volumeOK and (not useMACD or macdBull) and (not useBollFilter or bollRangeOK)
shortSignal = rsiMomentumShort and volumeOK and (not useMACD or macdBear) and (not useBollFilter or bollRangeOK)

// === Entry ===
if (longSignal)
    strategy.entry("Long", strategy.long)
if (shortSignal)
    strategy.entry("Short", strategy.short)

// === Exit: TP/SL or Trailing + Minimum Holding Period ===
barHoldMin = input.int(2, "Minimum holding period (candles)", minval=1)
var int entryBar = na

if (strategy.opentrades > 0)
    entryBar := na(entryBar) ? bar_index : entryBar
else
    entryBar := na

barsSinceEntry = bar_index - entryBar

if (strategy.position_size > 0 and barsSinceEntry >= barHoldMin)
    if useTrailing
        strategy.exit("Exit Long", from_entry="Long", trail_points=trailDist, trail_offset=trailDist)
    else
        strategy.exit("TP/SL Long", from_entry="Long", profit=tp, loss=slDist)
if (strategy.position_size < 0 and barsSinceEntry >= barHoldMin)
    if useTrailing
        strategy.exit("Exit Short", from_entry="Short", trail_points=trailDist, trail_offset=trailDist)
    else
        strategy.exit("TP/SL Short", from_entry="Short", profit=tp, loss=slDist)

// === Alerts ===
alertcondition(longSignal, title="BUY", message="NASDAQ BUY Signal aktiv!")
alertcondition(shortSignal, title="SELL", message="NASDAQ SELL Signal aktiv!")

Strategy parameters

The original address: Multi-Indicator Momentum Dynamic Trailing Quantitative Trading Strategy

Comments

[Rebecca Chow]

Great breakdown of the strategy — I like how you combined multiple indicators with dynamic trailing to adapt to different market conditions. The use of momentum filtering adds a nice layer of risk control too. Curious to see how it performs across different asset classes or during high-volatility periods. Thanks for sharing!