Dynamic Liquidity Cascade Capture Strategy

Pine ScriptMean reversion Volatility Tools & templates EMA SMA ATR

Author: ianzeng123
Complexity: Starter · 74 lines
Parameters: 0
Backtest bar: 1d · swing
Tested on: ETH/USDT · spot

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Overview

This strategy is a quantitative trading system specifically designed to capture extreme market volatility periods. It monitors the deviation between price and moving averages to identify potential market liquidity exhaustion situations and capture market reversal opportunities. The strategy incorporates moving average combinations, volatility tracking, and dynamic stop-loss mechanisms to build a complete trading system.

Strategy Principles

The core of the strategy is to identify market anomalies by calculating the deviation between price and moving averages. Specific implementation includes:

Using a combination of 15-period Simple Moving Average (SMA) and 30-period Exponential Moving Average (EMA) as benchmark prices
Calculating the percentage deviation between current price and moving average combination
Determining historical extremes through 89-period highs and lows
Entering long positions when three consecutive long-side liquidity exhaustion signals occur
Implementing a triple exit mechanism: technical rebound, reverse liquidity exhaustion signals, and trailing stop-loss

Strategy Advantages

Precise Market Timing: Multiple indicator confirmation improves entry accuracy
Comprehensive Risk Control: Multi-layered stop-loss mechanism effectively controls downside risk
High Adaptability: Strategy automatically adjusts stop-loss range based on market volatility
Strong Execution: Clear entry and exit conditions reduce subjective judgment
High Systematization: Entire trading process is based on quantitative indicators, easy to automate

Strategy Risks

False Signal Risk: May generate incorrect liquidity exhaustion signals in sideways markets
Slippage Risk: May face significant execution slippage under extreme market conditions
Parameter Sensitivity: Strategy performance is sensitive to moving average periods and stop-loss multipliers
Market Environment Dependency: Strategy returns may be suboptimal in low volatility environments
Technical Risk: System stability must be maintained to avoid signal delays or losses

Strategy Optimization Directions

Incorporate Volume Indicators: Confirm liquidity exhaustion signals through volume analysis
Optimize Parameter Adaptivity: Dynamically adjust strategy parameters based on market volatility states
Add Market Environment Filters: Pause trading in unsuitable market conditions
Enhance Stop-Loss Mechanism: Consider adding volatility-based dynamic stop-loss
Improve Signal Confirmation: Add more technical indicators to filter false signals

Summary

The Dynamic Liquidity Cascade Capture Strategy is a quantitative trading system focused on capturing extreme market situations. Through scientific indicator combinations and strict risk control, the strategy can capture trading opportunities during intense market volatility. While certain risks exist, through continuous optimization and improvement, the strategy has the potential to maintain stable performance across various market environments.

Source Pine Script

/*backtest
start: 2024-02-22 00:00:00
end: 2025-02-19 08:00:00
period: 1d
basePeriod: 1d
exchanges: [{"eid":"Binance","currency":"ETH_USDT"}]
*/

//@version=5
strategy("Liquidation Cascade Strategy", overlay=true)

// Paramètres de l'indicateur de liquidation
var float lastHigh = na
var float lastLow = na
var float lastPriceLow = na
var float lastPriceHigh = na
var bool shortLiq = na
var bool longLiq = na

src = close
maLength1 = 15
maLength2 = 30
ma1 = ta.sma(src, maLength1)
ma2 = ta.ema(src, maLength2)
avgLine = (ma1 + ma2) / 2
distVal = ((src - avgLine) / avgLine) * 100

ph = ta.highest(distVal, 89)
pl = ta.lowest(distVal, 89)

if ph == distVal and ph > 0 
    lastHigh := distVal
    lastPriceHigh := high

if pl == distVal and pl < 0 
    lastLow := distVal
    lastPriceLow := low

shortLiq := not na(lastHigh) and lastHigh == distVal and distVal > 0
longLiq := not na(lastLow) and lastLow == distVal and distVal < 0

// Condition d'achat : 3 liquidations longues consécutives
buyCondition = ta.valuewhen(longLiq, longLiq, 0) and ta.valuewhen(longLiq, longLiq, 1) and ta.valuewhen(longLiq, longLiq, 2)
if (buyCondition)
    strategy.entry("Buy", strategy.long)

// Conditions de vente
var float entryPrice = na
var bool positionOpen = false

// Mise à jour du prix d'entrée
if (buyCondition)
    entryPrice := close
    positionOpen := true

// 1. Vente sur rebond technique (distVal > -1%)
sellCondition1 = distVal > -1 and positionOpen

// 2. Vente sur liquidation courte
sellCondition2 = shortLiq and positionOpen

// 3. Trailing Stop (2x ATR)
atr = ta.atr(14)
trailingStop = close - 2 * atr
sellCondition3 = close < trailingStop and positionOpen

// Exécution des ventes
if (sellCondition1 or sellCondition2 or sellCondition3)
    strategy.close("Buy")
    positionOpen := false

// Visualisation
plot(avgLine, color=color.blue, title="Avg Line")
plot(distVal, color=distVal > 0 ? color.red : color.green, style=plot.style_columns)

Published by ianzeng123 on the FMZ strategy square · last modified 2025-02-24. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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