Liquidity Dynamic Risk Management Multi Factor Trading Strategy

Pine ScriptMachine learning Breakout Momentum RSI EMA SMA ATR Volume Pivots / S&R

Author: ianzeng123
Complexity: Intermediate · 217 lines
Parameters: 0
Backtest bar: 1h · intraday
Tested on: ETH/USDT · futures

Jump to code View in vault Original on FMZ

Overview

This is an innovative quantitative trading strategy that provides a structured approach to trading by integrating liquidity zone entries, ATR volatility thresholds, and dynamic risk management. The strategy combines multiple technical analysis indicators to identify high-probability trading opportunities and automatically calculate take profit and stop loss levels.

Strategy Principles

The core principles of the strategy are based on the following key elements:

Liquidity Zone Analysis: Identifying potential support and resistance areas by calculating the lowest and highest points over a specific period.
ATR Volatility Filtering: Using Average True Range (ATR) as a dynamic threshold for entry and risk management.
Trend Filtering: Combining 50-period Exponential Moving Average (EMA) and Relative Strength Index (RSI) to confirm market trend and momentum.
Dynamic Risk Management: Automatically calculating take profit and stop loss levels based on ATR, with flexible risk/reward ratio adjustment.

Strategy Advantages

Multi-Dimensional Signal Generation: Combining liquidity, volatility, and trend filtering to improve signal quality.
Adaptive Risk Management: Dynamically adjusting take profit and stop loss to effectively control trading risk.
Flexible Parameter Configuration: Customizable ATR length, liquidity period, and trading session.
Visualization Support: Providing visualization of liquidity lines and first candle level.
Performance Tracking: Built-in trading statistics table displaying win rate and trade outcomes directly on the chart.

Strategy Risks

Parameter Sensitivity: Strategy performance highly depends on parameter selection, requiring continuous backtesting and optimization.
Market Adaptability: Potential instability in markets with unclear trends or high volatility.
False Breakout Risk: Potential for misreported liquidity zone breakouts.
Trading Frequency: Session filtering and multiple conditions may reduce trading opportunities.
Backtest Bias: The 64% historical win rate may not fully represent future performance.

Strategy Optimization Directions

Machine Learning Integration: Introducing machine learning algorithms for dynamic parameter and signal generation adjustment.
Multi-Market Adaptability: Developing more universal parameter settings applicable to different markets and instruments.
Advanced Risk Management: Implementing more complex position sizing and risk allocation algorithms.
Signal Confirmation Mechanism: Adding additional confirmation indicators like volume or other technical indicators.
Real-Time Performance Monitoring: Developing real-time performance assessment and adaptive adjustment modules.

Summary

The ThinkTech AI Trading Strategy provides traders with a powerful quantitative trading tool through an innovative multi-factor approach. By leveraging liquidity analysis, volatility filtering, and dynamic risk management, the strategy aims to identify high-quality trading opportunities. However, traders must continuously backtest, optimize, and apply the strategy cautiously to fully realize its potential.

Source Pine Script

/*backtest
start: 2025-01-01 00:00:00
end: 2025-03-30 00:00:00
period: 1h
basePeriod: 1h
exchanges: [{"eid":"Futures_Binance","currency":"ETH_USDT"}]
*/

if high > ta.highest(high[1], 5)
    strategy.entry("Enter Long", strategy.long)
else if low < ta.lowest(low[1], 5)
    strategy.entry("Enter Short", strategy.short)//@version=6
strategy("ThinkTech AI Signals", overlay=true, initial_capital=100000, default_qty_type=strategy.percent_of_equity, default_qty_value=100)

//──────────────────────────────
// Input Settings
//──────────────────────────────
riskRewardRatio    = input.float(title="Risk/Reward Ratio", defval=2.0, minval=1.0, step=0.1)

// Inputs from second script
liquidity_period   = input.int(20, title="Liquidity Base Period")
atr_length         = input.int(14, title="ATR Length")
atr_threshold      = input.float(0.3, title="ATR Breakout Threshold")
take_profit_mult   = input.float(0.25, title="Take-Profit Multiplier")
stop_loss_mult     = input.float(0.75, title="Stop-Loss Multiplier")
vol_filter         = input.bool(true, title="Enable Volume Filter")
session_filter     = input.bool(true, title="Limit to Trading Session")

// Additional inputs for further filtering and settings
atrMultiplier      = input.float(title="ATR Multiplier for Stop Loss", defval=1.5, minval=0.1, step=0.1)
retestCushionLong  = input.float(title="Retest Cushion Factor for BUY", defval=1.0, minval=1.0, step=0.001)
retestCushionShort = input.float(title="Retest Cushion Factor for SELL", defval=1.0, minval=0.0, maxval=1.0, step=0.001)
useTrendFilter     = input.bool(title="Use 50 EMA Trend Filter", defval=true)
useRSIFilter       = input.bool(title="Use RSI Filter", defval=false)
rsiPeriod          = input.int(title="RSI Period", defval=14, minval=1)
rsiOversold        = input.float(title="RSI Oversold Level", defval=30.0, minval=1, maxval=50)
rsiOverbought      = input.float(title="RSI Overbought Level", defval=70.0, minval=50, maxval=100)

// Option to remove plotted lines
showLiquidityLines   = input.bool(true, title="Show Liquidity Lines")
showFirstCandleLines = input.bool(true, title="Show First Candle Level Lines")

//──────────────────────────────
// Calculations & Variables (Liquidity & ATR Based)
//──────────────────────────────
price        = close
atr          = ta.atr(atr_length)
vol_condition = volume > ta.sma(volume, 20)

// Define Liquidity Base and Apex for support/resistance
liquidity_base = ta.lowest(low, liquidity_period)
apex           = ta.highest(high, liquidity_period)
// Track recent highs and lows for filtering
hh = ta.highest(high, 30)
ll = ta.lowest(low, 30)

//──────────────────────────────
// Trade Stats Variables
//──────────────────────────────
var float take_profit = na
var float stop_loss   = na
var bool  in_trade    = false
var int   win_count   = 0
var int   loss_count  = 0

//──────────────────────────────
// ENTRY LOGIC (BUY) Based on Liquidity & ATR
//──────────────────────────────
breakout_up      = price > liquidity_base + (atr * atr_threshold)
point_of_release = ta.crossover(price, liquidity_base) and breakout_up
retest_buy       = price > liquidity_base and price < hh and not in_trade

// Session condition (e.g. trading between 9AM–12PM)
session_condition = (hour >= 9 and hour <= 12) or not session_filter

if (point_of_release or retest_buy) and (vol_condition or not vol_filter) and session_condition
    strategy.entry("Buy", strategy.long)
    take_profit := price + (atr * take_profit_mult)
    stop_loss   := liquidity_base - (atr * stop_loss_mult)
    in_trade    := true
    alert("Buy Signal - Price: " + str.tostring(price), alert.freq_once_per_bar)

//──────────────────────────────
// ENTRY LOGIC (SELL) Based on Liquidity & ATR
//──────────────────────────────
breakout_down = price < apex - (atr * atr_threshold)
clean_break   = ta.crossunder(price, apex) and breakout_down
retest_sell   = price < apex and price > ll and not in_trade

if (clean_break or retest_sell) and (vol_condition or not vol_filter) and session_condition
    strategy.entry("Sell", strategy.short)
    take_profit := price - (atr * take_profit_mult)
    stop_loss   := apex + (atr * stop_loss_mult)
    in_trade    := true
    alert("Sell Signal - Price: " + str.tostring(price), alert.freq_once_per_bar)

//──────────────────────────────
// EXIT LOGIC (For BUY & SELL)
//──────────────────────────────
if strategy.position_size > 0
    if price >= take_profit
        strategy.close("Buy", comment="Take Profit")
        win_count += 1
        in_trade := false
    if price <= stop_loss
        strategy.close("Buy", comment="Stop Loss")
        loss_count += 1
        in_trade := false

if strategy.position_size < 0
    if price <= take_profit
        strategy.close("Sell", comment="Take Profit")
        win_count += 1
        in_trade := false
    if price >= stop_loss
        strategy.close("Sell", comment="Stop Loss")
        loss_count += 1
        in_trade := false

//──────────────────────────────
// Plot Liquidity Lines (Support/Resistance)
//──────────────────────────────
plot(showLiquidityLines ? liquidity_base : na, color=color.green, title="Liquidity Base (Support)")
plot(showLiquidityLines ? apex : na, color=color.red, title="Apex (Resistance)")

//──────────────────────────────
// Debugging Signal Shapes
//──────────────────────────────
plotshape(series=point_of_release, location=location.belowbar, color=color.green, style=shape.triangleup, title="Buy Signal")
plotshape(series=clean_break, location=location.abovebar, color=color.red, style=shape.triangledown, title="Sell Signal")

//──────────────────────────────
// Win/Loss Table Display
//──────────────────────────────
var table t = table.new(position.top_right, 1, 2)
if bar_index == last_bar_index
    table.cell(t, 0, 0, "Wins: " + str.tostring(win_count), bgcolor=color.new(color.green, 80))
    table.cell(t, 0, 1, "Losses: " + str.tostring(loss_count), bgcolor=color.new(color.red, 80))

//──────────────────────────────
// Alert Conditions for Entries and Exits
//──────────────────────────────
alertcondition(point_of_release, title="Buy Alert", message="Buy Signal Triggered")
alertcondition(clean_break, title="Sell Alert", message="Sell Signal Triggered")
var int lastClosedTrades = 0
var bool exitSignal = false
if strategy.closedtrades > lastClosedTrades
    exitSignal := true
    lastClosedTrades := strategy.closedtrades
alertcondition(exitSignal, title="EXIT Signal", message="EXIT signal triggered: Trade has closed (TP or SL reached).")

//──────────────────────────────
// FIRST 15-MINUTE CANDLE CAPTURE (9:30–9:45 AM EST)
//──────────────────────────────
newDay = dayofmonth != dayofmonth[1]
var float fHigh = na
var float fLow  = na
var bool  firstCandleCaptured = false
if newDay
    fHigh := high
    fLow  := low
    firstCandleCaptured := true

//──────────────────────────────
// Additional Filters & Calculations
//──────────────────────────────
// Trend Filter: 50 EMA
ema50       = ta.ema(close, 50)
longFilter  = not useTrendFilter or (close > ema50)
shortFilter = not useTrendFilter or (close < ema50)

// ATR-Based Stop Loss Buffer
atrValue       = ta.atr(14)
stopLossBuffer = atrValue * atrMultiplier

// RSI Calculation (if enabled)
rsiValue = ta.rsi(close, rsiPeriod)

//──────────────────────────────
// ENTRY CONDITIONS (15-Minute Candle Retest)
//──────────────────────────────
// Breakout/Breakdown based on the first candle levels
buyBreakout   = firstCandleCaptured and ta.crossover(close, fHigh)
sellBreakdown = firstCandleCaptured and ta.crossunder(close, fLow)
// Retest conditions: price must retest the level after the breakout/breakdown
buyRetest  = firstCandleCaptured and (low <= fHigh) and (close > fHigh)
sellRetest = firstCandleCaptured and (high >= fLow) and (close < fLow)
// Final entry signals (with optional RSI filter)
buySignal  = buyBreakout and buyRetest and longFilter and (strategy.position_size == 0) and (not useRSIFilter or (rsiValue < rsiOversold))
sellSignal = sellBreakdown and sellRetest and shortFilter and (strategy.position_size == 0) and (not useRSIFilter or (rsiValue > rsiOverbought))

//──────────────────────────────
// Trade Parameters for 15-Minute Candle Retest
//──────────────────────────────
candleRange    = fHigh - fLow
stopLossBuy    = fLow - stopLossBuffer
takeProfitBuy  = fHigh + (candleRange * riskRewardRatio)
stopLossSell   = fHigh + stopLossBuffer
takeProfitSell = fLow - (candleRange * riskRewardRatio)

//──────────────────────────────
// Execute Trades for 15-Minute Candle Retest
//──────────────────────────────
if buySignal
    strategy.entry("BUY", strategy.long)
    strategy.exit("TP/SL BUY", "BUY", stop=stopLossBuy, limit=takeProfitBuy)

if sellSignal
    strategy.entry("SELL", strategy.short)
    strategy.exit("TP/SL SELL", "SELL", stop=stopLossSell, limit=takeProfitSell)

//──────────────────────────────
// Plot First Candle Level Lines (Optional)
//──────────────────────────────
plot(showFirstCandleLines and firstCandleCaptured ? fHigh : na, title="First Candle High", color=color.blue, linewidth=2)
plot(showFirstCandleLines and firstCandleCaptured ? fLow : na, title="First Candle Low", color=color.red, linewidth=2)

Published by ianzeng123 on the FMZ strategy square · last modified 2025-03-31. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

Build this with AgenKit

A spec is not a trading system. Paste this into your coding agent and AgenKit runs a specialist team — architecture, test-first build, risk gates, review — with paper trading on by default.

in Claude Code, Cursor, Codex & 4 more

/agenkit build a production-grade trading bot with a backtest engine, risk controls and paper trading by default from strategies/Liquidity-Dynamic-Risk-Management-Multi-Factor-Trading-Strategy.md

npx agenkit install engineering-kit in your project
Paste the command into your agent and approve each phase
Get tests, backtest, risk controls and a paper-trading switch

Get AgenKit How /agenkit works

Understand it first

Before you run this, read the lesson behind the idea and how to backtest it honestly. Specs like this one are the survivors of an unknown number of trials; deflate their results accordingly.