Institutional Market Maker Tracking Strategy with Dynamic Cost Averaging and Liquidity Flow Analysis

Pine ScriptMarket making Machine learning Trend following SMA Supertrend VWAP Pivots / S&R

Author: ianzeng123
Complexity: Starter · 75 lines
Parameters: 0
Backtest bar: 1d · swing
Tested on: ETH/USDT · spot

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Overview

This strategy is a trading system based on market maker behavior and institutional-level liquidity analysis. It identifies high-probability trading opportunities by tracking market liquidity indicators, order book imbalances, and market maker footprints. The strategy combines Dynamic Cost Averaging (DCAA) with a hedge flow system to minimize risks and maximize returns. The system completely abandons traditional technical indicators in favor of institutional-level market microstructure analysis.

Strategy Principles

The core of the strategy is tracking market maker behavior through multi-dimensional data:

Using VWAP (Volume Weighted Average Price) to confirm institutional absorption/distribution positions
Analyzing CVD (Cumulative Volume Delta) to detect actual strength comparison between bulls and bears
Combining order book data to identify liquidity traps and stop-loss hunting zones
Implementing dynamic cost averaging method to establish staged position building at key support levels
Utilizing a hedging system for risk management during extreme market volatility

Strategy Advantages

Entirely based on market microstructure, avoiding the lag of technical indicators
Ability to predict large price movements in advance through market maker behavior analysis
Dynamic cost averaging system enables gradual position building during downtrends, reducing overall position cost
Hedging system provides additional risk protection, especially during periods of extreme market volatility
Strategy can adapt to market conditions in real-time, not relying on static support/resistance levels

Strategy Risks

Requires high-quality real-time market data, sensitive to data latency
May struggle to accurately judge market maker intentions during extremely low liquidity periods
Over-reliance on market maker behavior analysis may lead to false signals under certain market conditions
Dynamic cost averaging system may accumulate significant losses in continuously declining markets
Hedging strategy costs may erode profits in ranging markets

Optimization Directions

Introduce machine learning algorithms to improve market maker behavior identification accuracy
Optimize capital allocation ratios in the dynamic cost averaging system
Add more market microstructure indicators to enhance signal reliability
Develop adaptive hedge ratio adjustment mechanisms
Establish more comprehensive risk control systems, especially under extreme market conditions

Summary

This is an institutional-grade trading strategy built on market microstructure foundations. Through deep analysis of market maker behavior, combined with dynamic cost averaging and hedging systems, the strategy maintains stability across different market environments. While implementation faces some technical and operational challenges, its core concepts and methodology have solid market microstructure foundations, showing potential for long-term stable profitability.

Source Pine Script

/*backtest
start: 2024-12-12 00:00:00
end: 2025-02-18 08:00:00
period: 1d
basePeriod: 1d
exchanges: [{"eid":"Binance","currency":"ETH_USDT"}]
*/

//@version=6
strategy("EDGE Market Maker Strategy – DCAA & HedgeFlow", overlay=true)

// ✅ Import Indicators  
vwapLine = ta.vwap
superTrend = ta.sma(close, 10)  // Replace with actual Supertrend formula if needed
volData = volume // Volume from current timeframe
cvdData = ta.cum(close - close[1]) // Approximation of CVD (Cumulative Volume Delta)
orderBlockHigh = ta.highest(high, 20) // Approximate Order Block Detection
orderBlockLow = ta.lowest(low, 20)

// ✅ Market Maker Buy Conditions  
longCondition = ta.crossover(close, vwapLine) and cvdData > cvdData[1] and volData > volData[1]
if longCondition
    strategy.entry("BUY", strategy.long)

// ✅ Market Maker Sell Conditions  
shortCondition = ta.crossunder(close, vwapLine) and cvdData < cvdData[1] and volData > volData[1]
if shortCondition
    strategy.entry("SELL", strategy.short)

// ✅ Order Block Confirmation (For Stronger Signals)  
longOB = longCondition and close > orderBlockHigh
shortOB = shortCondition and close < orderBlockLow

if longOB
    label.new(bar_index, high, "BUY (Order Block)", color=color.green, textcolor=color.white, style=label.style_label_down)

if shortOB
    label.new(bar_index, low, "SELL (Order Block)", color=color.red, textcolor=color.white, style=label.style_label_up)

// ✅ DCAA Levels – Adaptive Re-Entry Strategy  
dcaaBuy1 = close * 0.97  // First re-entry for long position (3% drop)
dcaaBuy2 = close * 0.94  // Second re-entry for long position (6% drop)
dcaaSell1 = close * 1.03 // First re-entry for short position (3% rise)
dcaaSell2 = close * 1.06 // Second re-entry for short position (6% rise)

if longCondition
    strategy.entry("DCAA_BUY_1", strategy.long, limit=dcaaBuy1)
    strategy.entry("DCAA_BUY_2", strategy.long, limit=dcaaBuy2)

if shortCondition
    strategy.entry("DCAA_SELL_1", strategy.short, limit=dcaaSell1)
    strategy.entry("DCAA_SELL_2", strategy.short, limit=dcaaSell2)

// ✅ HedgeFlow System – Dynamic Hedge Adjustments  
hedgeLong = ta.crossunder(close, superTrend) and cvdData < cvdData[1] and volData > volData[1]
hedgeShort = ta.crossover(close, superTrend) and cvdData > cvdData[1] and volData > volData[1]

if hedgeLong
    strategy.entry("HEDGE_LONG", strategy.long)

if hedgeShort
    strategy.entry("HEDGE_SHORT", strategy.short)

// ✅ Take Profit & Stop Loss  
tpLong = close * 1.05  
tpShort = close * 0.95  
slLong = close * 0.97  
slShort = close * 1.03  

strategy.exit("TP_Long", from_entry="BUY", limit=tpLong, stop=slLong)
strategy.exit("TP_Short", from_entry="SELL", limit=tpShort, stop=slShort)

// ✅ Plot VWAP & Supertrend for Reference  
plot(vwapLine, title="VWAP", color=color.blue, linewidth=2)
plot(superTrend, title="Supertrend", color=color.orange, linewidth=2)

Published by ianzeng123 on the FMZ strategy square · last modified 2025-02-27. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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