Overview
The Structural Breakout and Dynamic Risk Management Quantitative Trading Strategy is a trading system based on confirmed price structure, focusing on identifying breakouts of strong and weak highs and lows combined with dynamic risk management mechanisms. The core of the strategy is to identify market structure through swing highs and lows, executing trades only when price breaks through the most recent structural level (strong support or resistance). Additionally, the strategy incorporates a risk management system based on account equity, automatically calculating position size according to stop-loss distance, ensuring that risk for each trade is controlled within a preset range.

Strategy Principles
The strategy operates based on the following key principles:

1. Structure Identification Mechanism: The strategy uses pivot points to identify highs and lows in the market. Through the set swing length parameter (swingLength), the system can find peaks and troughs that meet the criteria.

2. Trend Direction Determination: The strategy determines trend direction by comparing consecutive highs and lows. When a new high is lower than the previous high, it is judged as a downtrend; when a new low is higher than the previous low, it is judged as an uptrend.

3. Strong/Weak Structure Classification: The system classifies highs and lows as "strong" or "weak." Highs in a downtrend are marked as "strong highs"; lows in an uptrend are marked as "strong lows."

4. Breakout Signal Generation: Buy signals are generated only when price breaks through a "strong high," and sell signals when breaking through a "strong low." This ensures that the trading direction is consistent with the overall market structure.

5. Dynamic Stop-Loss and Take-Profit: The strategy sets stop-loss levels based on the breakout position and adds a custom buffer to increase the safety margin. Take-profit targets are dynamically calculated based on the risk-reward ratio (RR).

6. Risk-Based Position Management: The system calculates position size for each trade based on account equity, risk percentage, stop-loss distance, and pip value, ensuring controlled risk.

7. The core logic in the code is manifested as: first detecting price swing points, then evaluating trend direction, followed by generating trading signals based on structural breakouts, and finally calculating appropriate stop-loss, take-profit targets, and position sizes.

Strategy Advantages
Analyzing the code implementation of this strategy, the following significant advantages can be summarized:

1. Structured Trading Decisions: The strategy makes trading decisions based on market structure rather than simple technical indicators, making the trading logic more aligned with the essential characteristics of the market and improving trade quality.

2. Confirmation-Based Entry Mechanism: Trades are executed only after price confirms a breakout of structural levels, reducing the risk of false breakouts.

3. Dynamic Risk Management: Stop-loss positions for each trade are set based on actual market structure rather than fixed points, better adapting to different market environments.

4. Proportional Risk Control: Through the percentage risk management method (riskPercent parameter), the strategy ensures that risk exposure for each trade is proportional to account size, effectively protecting capital.

5. Automatic Position Calculation: Position size is automatically adjusted according to stop-loss distance, maintaining consistent risk exposure in different volatility environments.

6. Single Position Control: The strategy limits to holding only one trade at a time, avoiding overtrading and risk accumulation.

7. Clear Visual Feedback: The system automatically plots entry points, stop-loss, and take-profit targets, allowing traders to clearly understand the risk and reward of each trade.

Strategy Risks
Despite the reasonable design of this strategy, the following potential risks still exist:

1. Parameter Sensitivity: The swing length (swingLength) parameter has a significant impact on strategy performance. Too small a value may lead to overtrading, while too large a value may miss important trading opportunities. It is recommended to find the most suitable parameter values for specific markets through backtesting.

2. Adaptability to Market Structure Changes: In rapidly changing market environments, historical structures may quickly become invalid. The strategy does not include market environment filtering mechanisms and may perform poorly in high-volatility or range-bound markets.

3. Slippage and Execution Risk: In actual trading, the execution price during breakouts may differ from the ideal price, affecting the accuracy of stop-loss and take-profit calculations.

4. Limitations of Fixed Risk-Reward Ratio: The strategy uses a fixed risk-reward ratio to set take-profit targets without considering actual market resistance/support levels, potentially leading to unreasonable take-profit targets.

5. Capital Management Assumptions: The strategy assumes that the pip value (pipValueUSD) is constant, but in reality, the pip value of some products will change with position size and market conditions.

Solutions include: adding market environment filters, adjusting parameters based on volatility, setting take-profit targets in conjunction with key price levels, and periodically reassessing and optimizing strategy parameters.

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

1. Market Environment Filtering: Add volatility indicators or trend strength filters to adjust trading strategy or pause trading in different market environments. This can be implemented by adding indicators such as ATR (Average True Range) or ADX (Average Directional Index).

2. Multi-Timeframe Confirmation: Introduce structural analysis from higher timeframes for trade direction filtering, ensuring that trading direction is consistent with larger trends, improving win rates.

3. Dynamic Risk-Reward Ratio: Dynamically adjust the risk-reward ratio based on market volatility or key price levels, rather than using a fixed value. Use higher RR in strong trend markets and more conservative RR in oscillating markets.

4. Partial Profit Mechanism: Implement a staged profit function, allowing partial profits to be locked in when reaching specific profit levels, while letting the remaining position continue to run.

5. Stop-Loss Movement Strategy: Add a trailing stop-loss function to protect existing profits as price moves in a favorable direction.

6. Entry Optimization: Add additional entry filter conditions, such as trading session filters, volume confirmation, or other technical indicator confirmations, to improve signal quality.

7. Capital Management Enhancement: Implement more complex capital management models, such as the Kelly Criterion or dynamic risk percentages considering historical win rates.

8. False Breakout Protection: Add mechanisms to protect against false breakouts, such as requiring price to maintain a certain time after breaking through structure or forming a confirming candle pattern.

These optimization directions aim to improve the robustness and adaptability of the strategy, enhancing risk management and entry quality while maintaining the original structured trading logic.

Summary
The Structural Breakout and Dynamic Risk Management Quantitative Trading Strategy is a trading system that combines technical analysis structure theory and modern risk management principles. By identifying key market structures and confirming breakouts, the strategy can capture high-quality trading opportunities while safeguarding capital through dynamic stop-losses, risk proportion control, and automated position calculation.

The main advantage of the strategy lies in its structured trading logic and strict risk control mechanisms, making it suitable for markets with obvious structural characteristics, such as precious metals, indices, and forex. However, the strategy also has potential risks in terms of parameter sensitivity and market adaptability.

By adding market environment filtering, multi-timeframe analysis, and dynamic risk management and other optimization measures, the robustness and profitability of the strategy can be further enhanced. Ultimately, the strategy provides a framework that balances trading opportunity capture and risk control, offering quantitative traders a reliable basis for a trading system.

Strategy source code

/*backtest
start: 2024-05-30 00:00:00
end: 2025-05-29 00:00:00
period: 2h
basePeriod: 2h
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=6
strategy("LANZ Strategy 4.0 [Backtest]", overlay=true, default_qty_type=strategy.cash, default_qty_value=100)

// === INPUTS ===
swingLength       = input.int(180, "Swing Length", minval=10)
slBufferPoints    = input.float(50.0, "SL Buffer (Points)", minval=0.1)
rr                = input.float(1.0, "TP Risk-Reward (RR)", minval=0.1)
riskPercent       = input.float(1.0, "Risk per Trade (%)", minval=0.1, maxval=100)
pipValueUSD       = input.float(10.0, "Pip Value in USD (1 lot)", minval=0.01)  // Para XAUUSD = $10/punto

// === PIVOT DETECTION ===
pivotHigh = ta.pivothigh(high, swingLength, swingLength)
pivotLow  = ta.pivotlow(low, swingLength, swingLength)

// === STATE TRACKING ===
var float lastTop = na
var float lastBottom = na
var float prevHigh = na
var float prevLow = na
var int trendDir = na
var bool topCrossed = false
var bool bottomCrossed = false
var bool topWasStrong = false
var bool bottomWasStrong = false

// === TREND EVALUATION ===
if not na(pivotHigh)
    prevHigh := lastTop
    lastTop := pivotHigh
    trendDir := (not na(prevHigh) and pivotHigh < prevHigh) ? -1 : trendDir
    topWasStrong := trendDir == -1
    topCrossed := false

if not na(pivotLow)
    prevLow := lastBottom
    lastBottom := pivotLow
    trendDir := (not na(prevLow) and pivotLow > prevLow) ? 1 : trendDir
    bottomWasStrong := trendDir == 1
    bottomCrossed := false

// === ENTRY SIGNALS ===
buySignal  = not topCrossed and close > lastTop
sellSignal = not bottomCrossed and close < lastBottom

// === ENTRY FREEZE VARIABLES ===
var float entryPriceBuy = na
var float entryPriceSell = na
var bool signalTriggeredBuy = false
var bool signalTriggeredSell = false

// === RESET ON POSITION CLOSE ===
if strategy.opentrades == 0
    signalTriggeredBuy := false
    signalTriggeredSell := false
    entryPriceBuy := na
    entryPriceSell := na

// === CAPTURE ENTRY PRICE ===
if buySignal and not signalTriggeredBuy and strategy.opentrades == 0
    entryPriceBuy := close
    signalTriggeredBuy := true

if sellSignal and not signalTriggeredSell and strategy.opentrades == 0
    entryPriceSell := close
    signalTriggeredSell := true

// === SL/TP / RIESGO DINÁMICO ===
pip = syminfo.mintick * 10
buffer = slBufferPoints * pip

var float sl = na
var float tp = na
var float qty = na

// === OBJETOS VISUALES ===
var line epLine = na
var line slLine = na
var line tpLine = na
var label epLabel = na
var label slLabel = na
var label tpLabel = na

// === BUY ENTRY ===
if signalTriggeredBuy and strategy.opentrades == 0
    sl := low - buffer
    tp := entryPriceBuy + (entryPriceBuy - sl) * rr
    slPips = math.abs(entryPriceBuy - sl) / pip
    riskUSD = strategy.equity * (riskPercent / 100)
    qty := slPips > 0 ? (riskUSD / (slPips * pipValueUSD)) : na
    strategy.entry("BUY", strategy.long, qty=qty)
    strategy.exit("TP/SL BUY", from_entry="BUY", stop=sl, limit=tp)
    topCrossed := true

// === SELL ENTRY ===
if signalTriggeredSell and strategy.opentrades == 0
    sl := high + buffer
    tp := entryPriceSell - (sl - entryPriceSell) * rr
    slPips = math.abs(entryPriceSell - sl) / pip
    riskUSD = strategy.equity * (riskPercent / 100)
    qty := slPips > 0 ? (riskUSD / (slPips * pipValueUSD)) : na
    strategy.entry("SELL", strategy.short, qty=qty)
    strategy.exit("TP/SL SELL", from_entry="SELL", stop=sl, limit=tp)
    bottomCrossed := true

Strategy parameters

The original address: Structural Breakout and Dynamic Risk Management Quantitative Trading Strategy