Strategy Overview
The Adaptive Momentum Cloud Breakout Quantitative Strategy is a dynamic breakout system that combines Kaufman's Adaptive Moving Average (KAMA), MACD histogram momentum filtering, and ATR-based cloud envelope bands. This strategy aims to identify directional price movements when price breaks through a volatility-defined "cloud" with momentum support. It is particularly suitable for intraday timeframes (15-minute, 1-hour) and stocks or crypto assets with trending potential.

The core logic relies on establishing a trend foundation through an adaptive moving average, confirming momentum direction with the MACD histogram, and setting dynamic volatility bands to determine breakout zones. Only when price breaks through these bands with sufficient momentum will a valid trading signal be triggered.

Strategy Principles
The core principles of this strategy are based on the synergistic effect of three key technical components:

Trend Foundation - KAMA (Kaufman's Adaptive Moving Average): The code manually implements the KAMA indicator by calculating an efficiency ratio to dynamically adjust the smoothing coefficient. When the market is in a clear trend, KAMA responds quickly; in sideways markets, it becomes more smooth, effectively filtering noise. The calculation process includes:

• Price change: The absolute difference between current price and price N periods ago
• Volatility: Sum of absolute daily price changes over N periods
• Efficiency ratio: Price change divided by volatility
• Smoothing coefficient: Based on a squared formula of the efficiency ratio

Momentum Confirmation - MACD Histogram: The strategy only allows long positions when the MACD histogram is positive and short positions when it's negative, avoiding entering fake breakouts without true momentum support. The MACD indicator identifies momentum changes by comparing the relationship between fast and slow exponential moving averages.

Cloud Envelope - ATR-Based Volatility Bands: The strategy draws two dynamic volatility bands around KAMA:

• Upper Band = KAMA + (ATR × Multiplier)
• Lower Band = KAMA - (ATR × Multiplier)

These bands automatically adjust their width according to market volatility, requiring larger breakouts to trigger signals when volatility increases.

Entry conditions strictly require multiple criteria to be met simultaneously:

• Long: Price crosses above the upper cloud band + MACD histogram is positive + Close is above KAMA
• Short: Price crosses below the lower cloud band + MACD histogram is negative + Close is below KAMA

The exit logic employs ATR-based adaptive volatility targets:

• Take Profit = Entry Price ± (ATR × TP Multiplier)
• Stop Loss = Entry Price ∓ (ATR × SL Multiplier)

This design ensures that profit and loss levels dynamically adjust to actual market volatility conditions, better aligning with market characteristics.

Strategy Advantages
After in-depth code analysis, this strategy demonstrates the following clear advantages:

1. Strong Adaptability: The core KAMA indicator automatically adjusts sensitivity based on market efficiency, responding quickly in trending markets while remaining stable in oscillating markets, effectively adapting to different market environments. The code achieves this through precise calculation of efficiency ratio and smoothing coefficient.

2. Multi-Layer Filtering Mechanism: The strategy combines price breakout, trend direction, and momentum confirmation as a triple verification mechanism, significantly reducing the risk of false breakouts. Signals are only triggered when price breaks through the cloud band, the MACD histogram shows appropriate momentum, and price is on the correct side of KAMA.

3. Dynamic Risk Management: The strategy employs an ATR-based adaptive stop-loss and take-profit mechanism, making risk control dynamically match market volatility. This avoids overreacting in low-volatility environments or underreacting in high-volatility environments.

4. High Visual Clarity: The strategy provides intuitive visual elements, including an orange KAMA line, green and red volatility bands, blue cloud fill, and background color changes based on the MACD histogram. These visual elements help traders quickly assess market conditions.

5. Integrated Capital Management: The strategy adopts a default risk management setting of 1% of account equity, ensuring that the risk exposure of each trade remains within a controllable range, contributing to the stability of long-term capital curves.

6. Parameter Adjustability: The strategy offers multiple adjustable parameters, including KAMA length, MACD parameters, ATR period, cloud multiplier, and take-profit/stop-loss multipliers, allowing traders to customize according to specific markets and personal risk preferences.

Strategy Risks
Despite its sophisticated design, the strategy still has the following potential risks:

1. False Breakout Risk: Despite having multi-layer filtering mechanisms, situations where price quickly retraces after a breakout can still occur in highly volatile markets. Solutions include adding confirmation factors, such as waiting for a retest of support/resistance after breakout, or adding volume confirmation.

2. Parameter Sensitivity: Strategy performance may be highly sensitive to settings such as KAMA length, cloud multiplier, and MACD parameters. Different markets and timeframes may require different parameter settings. It is recommended to optimize parameters for specific trading instruments through backtesting, avoiding overfitting.

3. Sluggish Response at Trend Turning Points: Since both KAMA and MACD are lagging indicators, they may not capture turning points in time when trends change dramatically. This can lead to significant drawdowns during the initial phase of trend reversals. Consider adding leading indicators such as RSI or candlestick pattern recognition for early warning.

4. Market Applicability Limitations: The strategy may produce more invalid signals in oscillating markets. Although the adaptive nature of KAMA mitigates this issue in the code, it is still recommended to prioritize application in markets with obvious trend characteristics.

5. Limitations of Fixed ATR Multipliers: Although ATR itself is adaptive, fixed ATR multipliers may not be suitable for all market environments. During extreme volatility periods, larger multipliers may be needed to avoid premature stop-losses, while smaller multipliers might be more appropriate during low volatility periods to capture more opportunities.

Strategy Optimization Directions
Based on code analysis, the strategy can be optimized in the following directions:

1. Dynamic Adjustment of Cloud Multiplier: The cloud multiplier can be dynamically adjusted according to market volatility states, for example, increasing the multiplier during high-volatility periods and decreasing it during low-volatility periods. This can be achieved by calculating the volatility of volatility or the ratio of long-term to short-term ATR.

2. Add Volume Confirmation: Adding volume expansion confirmation to entry conditions can significantly improve the reliability of breakout signals. This can be done by comparing current volume with the N-period average volume, confirming breakouts as valid only when volume significantly increases.

3. Introduce Trailing Stop-Loss: The current strategy uses fixed ATR multiples for stop-loss. Consider implementing a trailing stop-loss mechanism, such as a moving stop-loss based on KAMA or cloud bands, to protect more profits and improve the profit-to-loss ratio. This can be implemented using the trail_* parameters of strategy.exit in the code.

4. Time Filter: Introduce time filtering conditions to avoid known inefficient trading sessions, such as high-volatility periods before market opening and closing, or specific economic data release periods. This can be implemented by checking the time of the current bar.

5. Multi-Timeframe Confirmation: Combine the trend direction of higher timeframes, only trading in the direction consistent with higher timeframe trends. This requires using the request.security function to obtain indicator values from higher timeframes.

6. Machine Learning Optimization: Consider using machine learning techniques to dynamically optimize parameters or predict breakout success rates. For example, train a model based on historical data to predict the probability of successful breakouts under current market conditions, only entering when probability is high.

Summary
The Adaptive Momentum Cloud Breakout Quantitative Strategy is a well-designed trading system that effectively identifies price breakouts with momentum support by combining KAMA adaptive trend following, MACD momentum confirmation, and ATR-based dynamic volatility bands. The strategy is particularly suitable for markets with obvious trend characteristics and intraday trading timeframes.

The core advantages of the strategy lie in its adaptability and multi-layer filtering mechanism, which can dynamically adjust sensitivity according to market conditions and effectively reduce false signals. Meanwhile, ATR-based risk management ensures that profit and loss levels match actual market volatility.

Despite limitations such as parameter sensitivity and market applicability, the strategy's robustness and adaptability can be further enhanced through the suggested optimization directions, such as dynamic cloud multipliers, volume confirmation, and trailing stop-losses. Most importantly, traders should understand the logic behind the strategy, optimize parameters according to specific market characteristics, and strictly implement risk management rules to achieve long-term stable performance.

Through carefully designed visual elements and clear trading logic, this strategy is not only suitable for automated trading but also provides valuable decision support tools for manual traders. Both novices and experienced traders can benefit from this systematic approach to find high-probability trading opportunities in the market.

Strategy source code

/*backtest
start: 2024-06-18 00:00:00
end: 2025-06-16 08:00:00
period: 1h
basePeriod: 1h
exchanges: [{"eid":"Binance","currency":"ETH_USDT"}]
*/

//@version=6
strategy("AI Momentum Cloud v6", overlay=true, default_qty_type=strategy.percent_of_equity, default_qty_value=1)

// === INPUTS ===
src         = input.source(close, "Source")
lengthKAMA  = input.int(10, "KAMA Length")
lengthMACD  = input.int(12, "MACD Fast")
lengthSig   = input.int(26, "MACD Slow")
lengthHist  = input.int(9, "MACD Signal")
atrLen      = input.int(14, "ATR Length")
mult        = input.float(1.5, "Cloud Multiplier")
tpMult      = input.float(2.0, "Take Profit ATR")
slMult      = input.float(1.0, "Stop Loss ATR")

// === CUSTOM KAMA FUNCTION ===
priceChange = math.abs(src - src[lengthKAMA])
volatility = math.sum(math.abs(src - src[1]), lengthKAMA)
efficiencyRatio = volatility != 0 ? priceChange / volatility : 0
sc = math.pow(efficiencyRatio * 2 / (lengthKAMA + 1), 2)

kama = 0.0
kama := na(kama[1]) ? src : kama[1] + sc * (src - kama[1])

// === MACD Momentum ===
macdLine   = ta.ema(src, lengthMACD) - ta.ema(src, lengthSig)
macdSignal = ta.ema(macdLine, lengthHist)
macdHist   = macdLine - macdSignal

// === Cloud Bands (Dynamic Volatility Envelope) ===
atr        = ta.atr(atrLen)
cloudUpper = kama + atr * mult
cloudLower = kama - atr * mult

// === ENTRY CONDITIONS ===
longCond  = ta.crossover(close, cloudUpper) and macdHist > 0 and close > kama
shortCond = ta.crossunder(close, cloudLower) and macdHist < 0 and close < kama

if longCond
    strategy.entry("Long", strategy.long)
    strategy.exit("Exit Long", from_entry="Long", limit=close + atr * tpMult, stop=close - atr * slMult)

if shortCond
    strategy.entry("Short", strategy.short)
    strategy.exit("Exit Short", from_entry="Short", limit=close - atr * tpMult, stop=close + atr * slMult)

// === VISUALS ===
plot(kama, title="KAMA", color=color.orange, linewidth=2)
p1 = plot(cloudUpper, title="Cloud Upper", color=color.green, linewidth=1)
p2 = plot(cloudLower, title="Cloud Lower", color=color.red, linewidth=1)
fill(p1, p2, color=color.new(color.blue, 90), title="Cloud Fill")

bgcolor(macdHist > 0 ? color.new(color.green, 85) : macdHist < 0 ? color.new(color.red, 85) : na)

Strategy parameters

The original address: Adaptive Momentum Cloud Breakout Quantitative Strategy