Overview
The Multi-Indicator Momentum Breakout Strategy with Adaptive Risk Management is a trading system that combines multiple technical indicators to enter positions based on daily momentum breakout signals. This strategy employs a comprehensive analysis through EMA combinations, ATR volatility, volume confirmation, and candlestick patterns to identify strong trends while implementing precise risk control and profit management. The core concept is to track markets with upward momentum while limiting risk exposure per trade through an adaptive position sizing method. The strategy design strictly follows trend-following and risk management principles, making it suitable for medium to long-term traders.

Strategy Principles
The strategy builds a reliable entry system by analyzing multiple technical indicators, requiring four key conditions to be simultaneously met:

1. EMA Trend Confirmation: The daily 50-day EMA must be above the 100-day EMA, ensuring an upward medium-term trend.
2. ATR Breakout Confirmation: The daily closing price must be higher than (10-day EMA + 10-day ATR), indicating that the price has sufficient strength to break through the volatility range.
3. Candlestick Confirmation: The daily closing price must be higher than the opening price, confirming intraday bullish momentum.
4. Volume Confirmation: The daily volume must be higher than the 12-day volume EMA, ensuring adequate market participation.

Only when all conditions are simultaneously satisfied will the strategy generate an entry signal. After entry, the risk management system automatically sets the stop-loss at (10-day EMA - 10-day ATR), which typically represents a support area, while setting the profit target at (10-day EMA + 3*10-day ATR). This design achieves a risk-reward ratio of approximately 1:3.

Notably, the strategy employs an adaptive position sizing method based on strategy equity, limiting the risk of each trade to 2% of the account value through the formula: Number of shares = Risk amount / Risk per share, ensuring controllable risk.

Strategy Advantages

1. Multi-dimensional Signal Confirmation: By combining EMA systems, ATR bands, candlestick patterns, and volume, the strategy significantly reduces false signals and improves trade quality.

2. Adaptive Position Management: The strategy automatically adjusts trading quantity based on current market volatility and account size, maintaining consistent risk exposure across different market environments. This means reducing positions in high-volatility markets and moderately increasing positions in low-volatility markets.

3. Clear Risk Control: Each trade's risk is strictly limited to 2% of the account value, aligning with professional risk management best practices.

4. Objective Entry and Exit Criteria: The strategy rules are fully quantified, eliminating subjective judgment factors and helping traders maintain discipline.

5. Adaptation to Trending Markets: Through the combination of momentum indicators and breakout confirmation, the strategy excels in clearly trending market environments.

6. Visualization of Trading Signals: The strategy provides background color markers and graphical indicators, intuitively displaying entry signals while clearly marking stop-loss and profit target levels during trades.

Strategy Risks

1. Trend Reversal Risk: Despite using multiple confirmation mechanisms, the strategy may still face significant losses during sudden market reversals. The solution is to consider early exits when intraday reversal patterns form or to add trend strength filters.

2. False Breakout Risk: Markets sometimes experience quick retracements after breakouts. This risk can be mitigated by adding time filters (requiring signals to persist for a certain period) or seeking confirmation signals in lower timeframes.

3. Stop-Loss Placement Risk: ATR-based stops may be too loose during high-volatility periods, causing single losses to exceed expectations. Consider setting maximum stop-loss limits or optimizing stops using key support levels.

4. Liquidity Risk: The strategy does not consider liquidity factors, which may lead to slippage or inability to execute trades at expected prices in low-liquidity markets. It is recommended to add liquidity filtering conditions and avoid trading low-liquidity instruments.

5. Systemic Risk: When markets generally show highly correlated declines, multiple trades may trigger stop-losses simultaneously. This can be controlled by adding cross-market correlation filters and limiting overall positions during high-correlation periods.

Strategy Optimization Directions

1. Dynamic Stop-Loss Mechanism: The current strategy uses fixed ATR multiples for stop-losses. Consider implementing a trailing stop mechanism that automatically adjusts stop levels as prices move favorably, locking in partial profits while giving trades enough room to breathe.

2. Partial Profit Strategy: The current strategy employs a single profit target. It can be optimized to a partial profit model, such as closing 1/3 of the position at 1ATR, 2ATR, and 3ATR respectively. This approach locks in partial profits early while allowing remaining positions to capture larger profit potential.

3. Add Market Environment Filtering: Add overall market direction and volatility assessment, only trading in favorable market environments. For example, incorporate VIX-type volatility indicators or market breadth indicators to avoid entering during extreme market conditions.

4. Optimize EMA Parameters: Through backtesting, find optimal EMA parameter combinations for different instruments and market environments, rather than using fixed 10/50/100-day parameters.

5. Add Time Filtering Conditions: Add time-related filtering conditions, such as avoiding important economic data releases or specific market sessions, reducing volatility risks caused by sudden events.

6. Risk Balance Optimization: The current position sizing calculation is based on single-asset volatility. Introduce portfolio-level risk management, considering correlation and overall risk exposure for more comprehensive risk control.

Summary
The Multi-Indicator Momentum Breakout Strategy with Adaptive Risk Management builds a trading system with clear rules by comprehensively applying multiple dimensions of technical analysis. The strategy's greatest advantage lies in combining momentum breakout signals with strict risk management, pursuing market trend returns while strictly controlling the risk of each trade. Through multiple filters of EMA combinations, ATR bands, candlestick patterns, and volume confirmation, the strategy can screen out high-quality trading signals. Meanwhile, account percentage-based adaptive position management ensures consistent risk control principles across different market environments and instruments.

Although the strategy has a relatively complete design, risks still exist in trend reversals, false breakouts, and liquidity. Future optimizations could include introducing dynamic stop-losses, partial profits, and market environment filtering. For investors pursuing medium to long-term trading opportunities, this strategy provides a trading framework that combines systematization and adaptability, reducing emotional interference while adapting to changes in different market environments.

Strategy source code

/*backtest
start: 2024-04-27 00:00:00
end: 2025-04-25 08:00:00
period: 1d
basePeriod: 1d
exchanges: [{"eid":"Futures_Binance","currency":"SOL_USDT"}]
*/

// This Pine Script® code is subject to the terms of the Mozilla Public License 2.0 at https://mozilla.org/MPL/2.0/
// © avi

//@version=5
strategy("AVI - S13", overlay=true, initial_capital=10000, default_qty_type=strategy.fixed)

// Get daily-level values
dailyATR      = request.security(syminfo.tickerid, "D", ta.atr(10))
dailyEMA10    = request.security(syminfo.tickerid, "D", ta.ema(close, 10))
dailyEMA50    = request.security(syminfo.tickerid, "D", ta.ema(close, 50))
dailyEMA100   = request.security(syminfo.tickerid, "D", ta.ema(close, 100))
dailyClose    = request.security(syminfo.tickerid, "D", close)
dailyOpen     = request.security(syminfo.tickerid, "D", open)
dailyVol      = request.security(syminfo.tickerid, "D", volume)
dailyVolEMA12 = request.security(syminfo.tickerid, "D", ta.ema(volume, 12))

ema_plus_atr   = dailyEMA10 + dailyATR
ema_minus_atr  = dailyEMA10 - dailyATR
ema_plus_atr1  = dailyEMA10 + dailyATR * 3

// Entry conditions
conditionema    = dailyEMA50 > dailyEMA100
conditionatr    = dailyClose > ema_plus_atr
conditioncandel = dailyClose > dailyOpen
conditionvol    = dailyVol > dailyVolEMA12
entryCondition  = conditionema and conditionatr and conditioncandel and conditionvol

bgcolor(entryCondition ? color.new(#26e600, 90) : na)
plotshape(entryCondition, location=location.belowbar, style=shape.labelup, color=color.green, size=size.tiny, title="Entry")

// Trade management variables
var bool inTrade = false
var float entryPrice = na
var float stopLossPrice = na
var float takeProfitPrice = na
var int entryBar = na

// Entry logic
if entryCondition and not inTrade and timeframe.isdaily
    stopLossPrice := ema_minus_atr
    takeProfitPrice := ema_plus_atr1
    riskPerShare = math.abs(dailyClose - stopLossPrice)
    riskAmount = strategy.equity * 0.02
    sharesCount = riskPerShare > 0 ? math.floor(riskAmount / riskPerShare) : 0

    if sharesCount > 0
        strategy.entry("Long", strategy.long, qty=sharesCount)
        entryPrice := dailyClose
        inTrade := true
        entryBar := bar_index

// Exit logic
if inTrade
    if low <= stopLossPrice
        strategy.close("Long", comment="SL")
        inTrade := false
    else if high >= takeProfitPrice
        strategy.close("Long", comment="TP")
        inTrade := false

// Draw horizontal lines for SL and TP during the trade
plot(inTrade ? stopLossPrice : na, title="Stop Loss", color=color.red, linewidth=1, style=plot.style_linebr)
plot(inTrade ? takeProfitPrice : na, title="Take Profit", color=color.green, linewidth=1, style=plot.style_linebr)

The original address: Multi-Indicator Momentum Breakout Strategy with Adaptive Risk Management