Multi-Dimensional Dynamic Liquidity Sweep Quantitative Strategy

Overview
The Multi-Dimensional Dynamic Liquidity Sweep Quantitative Strategy is an advanced trading system specifically designed to detect and capitalize on stop-loss hunting behavior in markets. This strategy is based on the phenomenon where institutional players often create false breakouts beyond key liquidity areas (such as recent highs or lows) followed by rapid reversals. The strategy is particularly effective when markets trigger clusters of stop-loss orders and then reverse direction. It combines price breakouts, RSI indicators, volume spike confirmation, and an ATR-based dynamic stop-loss/take-profit management system to form a comprehensive trading framework.

Strategy Principles
The core principle of this strategy is to identify and capitalize on so-called "liquidity sweeps" or "stop hunts." The implementation is as follows:

Liquidity Zone Identification: The strategy uses a lookback period (default 20 periods) to determine recent highs and lows, which typically accumulate significant stop-loss orders.

Breakout Detection: When current prices break beyond previous highs or lows, the strategy detects potential liquidity sweep events.

• High breakout: high > highestHigh[1]
• Low breakout: low < lowestLow[1]

Filtering Conditions: To reduce false signals, the strategy introduces two key filters:

• RSI Confirmation: Requires RSI to be in oversold territory (<40) during low breakouts and overbought territory (>60) during high breakouts
• Volume Confirmation: Requires volume to be significantly above average (greater than 1.5x the 20-day volume SMA)

Entry Signals:

• Long condition: Price breaks below lower liquidity zone + RSI oversold + Volume spike
• Short condition: Price breaks above upper liquidity zone + RSI overbought + Volume spike

Risk Management: The strategy employs ATR-based dynamic stop-loss and take-profit settings:

• Stop-loss placement: Set at 1.5x the current ATR distance
• Take-profit placement: Similarly based on 1.5x the current ATR

Trade Tracking: The strategy tracks position changes and marks entry and exit points on the chart, providing intuitive visual feedback on trades.

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

1. Market Psychology Insight: The strategy captures the psychological weakness of market participants, specifically the concentrated behavior of setting stop-losses at key levels, which is a pattern that repeats in markets.

2. Multiple Confirmation Mechanism: Combines price action (breakouts), technical indicators (RSI), and volume analysis to form a triple confirmation system, greatly reducing false signals.

3. Dynamic Risk Management: Uses ATR for stop-loss and take-profit settings, allowing risk management to adapt to changes in market volatility, setting wider stops in high-volatility markets and narrower stops in low-volatility markets.

4. Objective Entry Conditions: The strategy's entry conditions are entirely based on objective technical indicators and market behavior, reducing the interference of subjective judgment.

5. Visual Feedback System: By marking entry and exit points on the chart, traders can visually evaluate strategy performance and conduct review analysis.

6. Adaptability to Different Market Environments: Through adjustable parameter settings, the strategy can adapt to different market environments and trading instruments.

Strategy Risks
Despite its sophisticated design, the strategy still has the following risk points:

1. False Breakout Risk: Markets may continue moving in one direction after a breakout rather than reversing as expected, which would trigger stop-losses. The solution is to optimize the lookback period parameter or add additional trend filters.

2. Parameter Sensitivity: Strategy performance is sensitive to parameter settings (such as lookback period, ATR multiplier, RSI thresholds). It is recommended to adjust optimal parameters for different markets and timeframes through backtesting.

3. Market Environment Dependency: This strategy performs best in ranging markets and may generate frequent false signals in strong trending markets. Consider adding trend recognition components to mitigate this risk.

4. Volume Anomalies: In certain markets or special trading days, volume may exhibit anomalies due to non-regular factors (such as holidays, policy announcements) affecting signal quality. Consider using relative volume or adjusting the volume spike multiplier.

5. Slippage Risk: In high-volatility events, actual execution prices may differ significantly from theoretical entry prices. Consider additional slippage protection measures in live trading.

Strategy Optimization Directions
Based on code analysis, here are several possible optimization directions:

1. Add Trend Filters: Introduce trend recognition components (such as moving averages, ADX indicator, etc.) to only enter trades when the trend direction aligns with the entry signal, avoiding counter-trend trades in strong trends.

2. Dynamic Parameter Adjustment: Introduce adaptive mechanisms to automatically adjust the lookback period and ATR multiplier based on market volatility, allowing the strategy to better adapt to different market states.

3. Enhanced Volume Analysis: Consider using relative volume change rates or volume profile analysis rather than simple volume average comparisons to obtain more precise volume confirmation.

4. Time Filtering: Add trading time filters to avoid abnormally volatile market opening and closing periods, or specific economic data release times.

5. Multi-Timeframe Analysis: Integrate market structure analysis from higher timeframes, only seeking trading opportunities near support and resistance areas in higher timeframes.

6. Optimize Take-Profit Strategy: Consider implementing a stepped take-profit strategy, moving the stop-loss to breakeven after achieving a certain profit, creating risk-free trades.

7. Machine Learning Enhancement: Enhance parameter selection and signal generation processes by introducing machine learning algorithms to learn from historical liquidity sweep patterns.

Summary
The Multi-Dimensional Dynamic Liquidity Sweep Quantitative Strategy is a carefully designed trading system aimed at capturing common stop-loss hunting behavior in markets. By combining price breakouts, RSI indicators, and volume analysis, the strategy can effectively identify false breakouts and enter trades during price reversals. The strategy's dynamic risk management system based on the ATR indicator can adapt to different market volatility conditions.

While the strategy performs excellently in ranging markets, it may face challenges in strong trending environments. By adding trend filters, optimizing parameter settings, and enhancing volume analysis, the stability and profitability of the strategy can be further improved. Most importantly, traders need to understand the market mechanisms behind the strategy and make appropriate adjustments based on specific trading environments and personal risk preferences.

Overall, this is a trading strategy with a solid theoretical foundation and practicality, suitable for medium to long-term investors and day traders to apply in various market environments. Through continuous optimization and appropriate risk management, this strategy has the potential to become a powerful tool in a trading portfolio.

Strategy source code

/*backtest
start: 2024-06-30 00:00:00
end: 2025-01-31 00:00:00
period: 1d
basePeriod: 1d
exchanges: [{"eid":"Futures_Binance","currency":"ETH_USDT"}]
*/

//@version=5
strategy("Liquidity Sweep Strategy v2 - Fixed Close Labels", overlay=true, default_qty_type=strategy.percent_of_equity, default_qty_value=100)

// === INPUTS ===
lookback = input.int(20, title="Lookback for High/Low Sweep")
atrMult = input.float(1.5, title="ATR Multiplier for TP/SL")
volumeMult = input.float(1.5, title="Volume Spike Multiplier")
rsiPeriod = input.int(14, title="RSI Period")
rsiOB = input.int(60, title="RSI Overbought")
rsiOS = input.int(40, title="RSI Oversold")

// === CALCULATIONS ===
highestHigh = ta.highest(high, lookback)
lowestLow = ta.lowest(low, lookback)
sweepHigh = high > highestHigh[1]
sweepLow = low < lowestLow[1]

volMA = ta.sma(volume, 20)
volSpike = volume > volMA * volumeMult

rsi = ta.rsi(close, rsiPeriod)

atr = ta.atr(14)
longSL = low - atr * atrMult
longTP = close + atr * atrMult
shortSL = high + atr * atrMult
shortTP = close - atr * atrMult

// === ENTRY CONDITIONS ===
longEntry = sweepLow and rsi < rsiOS and volSpike
shortEntry = sweepHigh and rsi > rsiOB and volSpike

// === STRATEGY EXECUTION ===
if (longEntry)
    strategy.entry("Long", strategy.long)
    strategy.exit("Long TP/SL", from_entry="Long", stop=longSL, limit=longTP)
    label.new(bar_index, low, "🟢 BUY", style=label.style_label_up, textcolor=color.white, color=color.green, size=size.small)

if (shortEntry)
    strategy.entry("Short", strategy.short)
    strategy.exit("Short TP/SL", from_entry="Short", stop=shortSL, limit=shortTP)
    label.new(bar_index, high, "🔴 SELL", style=label.style_label_down, textcolor=color.white, color=color.red, size=size.small)

// === EXIT LABELS USING POSITION TRACKING ===
var float previous_position = na
position_closed = (strategy.position_size == 0 and previous_position != 0)

if position_closed and previous_position > 0
    label.new(bar_index, high, "🟩 SELL CLOSE", style=label.style_label_down, textcolor=color.white, color=color.green, size=size.small)
if position_closed and previous_position < 0
    label.new(bar_index, low, "🟥 BUY CLOSE", style=label.style_label_up, textcolor=color.white, color=color.red, size=size.small)

previous_position := strategy.position_size

Strategy parameters

The original address: Multi-Dimensional Dynamic Liquidity Sweep Quantitative Strategy

Comments

[Rebecca Chow]

Wow, a volatility-adaptive trend-following system? Sounds fancier than my 'buy low, sell high' strategy (which somehow always becomes 'buy high, panic sell low'). Jokes aside, the dual HMA approach looks slick! Though I suspect my cat could randomly walk on my keyboard and still outperform 90% of trading algorithms. Seriously though, great breakdown - now if only it could also predict when my coffee will go cold...