Overview
The Adaptive Triple-Confirmation Breakout Tracking Strategy is a quantitative trading approach that combines classical technical analysis theories with modern risk management techniques. This strategy integrates Jesse Livermore's breakout theory, Ed Seykota's trend confirmation methodology, and Paul Tudor Jones' ATR risk management principles, utilizing multiple condition filters and dynamic stop-loss mechanisms to capture high-probability trend breakout opportunities. The strategy employs a comprehensive approach combining pivot point breakouts, exponential moving average trend confirmation, volume validation, and ATR adaptive risk control, achieving an organic integration of traditional technical analysis with modern quantitative risk management.

Strategy Principles
The core principle of this strategy is built upon a multi-layered technical analysis confirmation mechanism. First, the strategy identifies recent pivot highs and lows to determine key support and resistance levels. When prices break through these critical levels, entry decisions are made in conjunction with trend confirmation conditions. For long signals, the strategy requires the closing price to break above recent pivot highs, with price above the 50-period EMA, 20-period EMA above 200-period EMA confirming an uptrend, and current volume exceeding the 20-period simple moving average to validate breakout effectiveness. Short conditions are reversed, requiring price to break below pivot lows, be below the 50-period EMA, 20-period EMA below 200-period EMA confirming a downtrend, accompanied by volume expansion. For risk management, the strategy uses 3 times ATR as the initial stop-loss distance and sets a 2 times ATR trailing stop offset for dynamic risk control.

Strategy Advantages
This strategy possesses multiple technical advantages, primarily demonstrated through its multi-confirmation mechanism. Through triple verification of pivot point breakouts, trend filtering, and volume confirmation, it significantly improves trading signal reliability and reduces false breakout probability. Secondly, the strategy's adaptability is outstanding, with ATR indicators enabling stop-loss levels to automatically adjust based on market volatility, providing wider stop-loss space during high volatility periods and tightening risk control during low volatility periods. The trailing stop mechanism ensures the strategy can maximize trend profit capture while protecting accrued gains. The strategy's parameter settings are flexible, allowing users to adjust according to different market environments and personal risk preferences. Additionally, this strategy integrates core concepts from three legendary traders, providing deep theoretical foundation and practical validation. Volume confirmation mechanisms enhance breakout signal authenticity, avoiding interference from low-volume false breakouts.

Strategy Risks
Despite the strategy's comprehensive design, several potential risks require attention. First is sideways market risk, where frequent false breakouts during market consolidation phases may lead to consecutive small losses. The solution involves adding additional market environment filters, such as ADX indicators to assess trend strength. Second is parameter sensitivity risk, where different parameter settings may result in significantly varying strategy performance, requiring backtesting optimization to find optimal parameter combinations. Slippage and execution risks cannot be ignored, particularly during rapid breakouts where actual execution prices may deviate from ideal prices. Furthermore, the strategy has limited adaptability to abnormal market events, such as gap openings caused by sudden news that may trigger unfavorable trading signals. Over-optimization risk also exists, where excessive fitting to historical data may lead to poor live trading performance. To mitigate these risks, recommendations include combining multi-timeframe analysis, adding fundamental filtering conditions, and setting maximum drawdown limits.

Strategy Optimization Directions
Strategy optimization should unfold across multiple dimensions to enhance overall performance. First, multi-timeframe analysis can be introduced, confirming trend direction on higher timeframes before seeking entry opportunities on lower timeframes, improving trade success rates and reducing counter-trend trading. Second, adding market environment identification modules through volatility indicators and trend strength indicators to assess current market conditions, pausing trading in environments unsuitable for breakout strategies. Dynamic parameter adjustment mechanisms are also important, automatically adjusting key parameters like EMA periods and ATR multiples based on market volatility and trend characteristics. Additionally, machine learning elements could be considered, training models through historical data to predict breakout success probability and improve signal quality. For risk management, implementing composite stop-loss mechanisms combining time stops, percentage stops, and volatility stops provides more comprehensive risk protection. Finally, deepening volume analysis by considering not only volume magnitude but also volume distribution and price-volume relationships further enhances breakout signal reliability.

Summary
The Adaptive Triple-Confirmation Breakout Tracking Strategy represents a typical application of combining technical analysis with quantitative trading. By integrating multiple technical elements including pivot point breakouts, trend confirmation, volume validation, and ATR risk management, the strategy constructs a relatively complete trading system. Its greatest highlight lies in the multi-confirmation mechanism and adaptive risk management, ensuring both trading signal quality and flexible risk control. However, successful strategy implementation still requires careful parameter optimization, strict risk management, and continuous performance monitoring. In practical application, it is recommended to combine additional market analysis tools and risk control measures, adjusting according to different market environments and trading instrument characteristics. Overall, this strategy provides investors pursuing trend breakout trading with a quantitative trading framework that possesses both theoretical foundation and practical value.

Strategy source code

/*backtest
start: 2024-05-22 00:00:00
end: 2025-05-20 08:00:00
period: 1d
basePeriod: 1d
exchanges: [{"eid":"Futures_Binance","currency":"ETH_USDT"}]
*/

//@version=6
strategy("V2_Livermore-Seykota Breakout", overlay=true, default_qty_type=strategy.percent_of_equity, default_qty_value=10)

// === Input Parameters ===
pivotLeft      = input.int(5,   "Pivot Left Bars",    minval=1)
pivotRight     = input.int(5,   "Pivot Right Bars",   minval=1)
emaFastLen     = input.int(20,  "Fast EMA Length")
emaMainLen     = input.int(50,  "Main EMA Length")
emaSlowLen     = input.int(200, "Slow EMA Length")
volLen         = input.int(20,  "Volume SMA Length")
atrLen         = input.int(14,  "ATR Length")
atrStopMul     = input.float(3.0, "ATR Stop-Loss Multiplier",    step=0.1)
atrTrailOffset = input.float(3.0, "ATR Trailing Offset Multiplier", step=0.1)
atrTrailMul    = input.float(3.0, "ATR Trailing Multiplier",      step=0.1)

// === Indicator Calculations ===
emaFast = ta.ema(close, emaFastLen)
emaMain = ta.ema(close, emaMainLen)
emaSlow = ta.ema(close, emaSlowLen)
volMA   = ta.sma(volume, volLen)
atrVal  = ta.atr(atrLen)

// === Detect Nearest Pivot High/Low ===
var float pivotHighVal = na
var float pivotLowVal  = na
ph = ta.pivothigh(high, pivotLeft, pivotRight)
pl = ta.pivotlow(low,  pivotLeft, pivotRight)
if not na(ph)
    pivotHighVal := ph
if not na(pl)
    pivotLowVal  := pl

// === Entry Conditions ===
longCond  = not na(pivotHighVal) and ta.crossover(close, pivotHighVal) and (close > emaMain) and (emaFast > emaSlow) and (volume > volMA)
shortCond = not na(pivotLowVal)  and ta.crossunder(close, pivotLowVal)  and (close < emaMain) and (emaFast < emaSlow) and (volume > volMA)

// Execute Entry Orders (only one position at a time)
if (longCond and strategy.position_size == 0)
    strategy.entry("Long", strategy.long)
    pivotHighVal := na  // reset pivot high after entry
if (shortCond and strategy.position_size == 0)
    strategy.entry("Short", strategy.short)
    pivotLowVal  := na  // reset pivot low after entry

// === Stop-Loss Based on ATR ===
longStop  = strategy.position_avg_price - atrVal * atrStopMul
shortStop = strategy.position_avg_price + atrVal * atrStopMul

// Exit Orders with ATR-Based Stop-Loss and Trailing Stop
strategy.exit("Exit Long", from_entry="Long", stop=longStop,  trail_offset=atrVal * atrTrailOffset, trail_points=atrVal * atrTrailMul)
strategy.exit("Exit Short", from_entry="Short", stop=shortStop, trail_offset=atrVal * atrTrailOffset, trail_points=atrVal * atrTrailMul)

Strategy parameters

The original address: Adaptive Triple-Confirmation Breakout Tracking Strategy