Account GuardianAccount Guardian: Dynamic Risk/Reward Overlay
Introduction
Account Guardian is an open-source indicator for TradingView designed to help traders evaluate trade setups before entering positions. It automatically calculates Risk-to-Reward ratios based on market structure, displays visual Stop Loss and Take Profit zones, and provides real-time position sizing recommendations.
The indicator addresses a fundamental question every trader should ask before entering a trade: "Does this setup make mathematical sense?" Account Guardian answers this question visually and numerically, helping traders avoid impulsive entries with poor risk profiles.
Core Functionality
Account Guardian performs four primary functions:
Detects swing highs and swing lows to identify logical stop loss placement levels
Calculates Risk-to-Reward ratios for both long and short setups in real-time
Displays visual SL/TP zones on the chart for immediate trade planning
Computes position sizing based on your account size and risk tolerance
The goal is to provide traders with instant feedback on whether a potential trade meets their minimum risk/reward criteria before committing capital.
How It Works
Swing Detection
The indicator uses pivot point detection to identify recent swing highs and swing lows on the chart. These swing points serve as logical areas for stop loss placement:
For Long Trades: The most recent swing low becomes the stop loss level. Price breaking below this level would invalidate the bullish thesis.
For Short Trades: The most recent swing high becomes the stop loss level. Price breaking above this level would invalidate the bearish thesis.
The swing detection lookback period is configurable, allowing you to adjust sensitivity based on your trading timeframe and style.
It automatically adjusts the tp and sl when it is applied to your chart so it is always moving up and down!
Risk/Reward Calculation
Once swing levels are identified, the indicator calculates:
Entry Price: Current close price (where you would enter)
Stop Loss: Recent swing low (for longs) or swing high (for shorts)
Risk: Distance from entry to stop loss
Take Profit: Entry plus (Risk × Target Multiplier)
R:R Ratio: Reward divided by Risk
The R:R ratio is then evaluated against your configured thresholds to determine if the setup is valid, marginal, or poor.
Visual Elements
SL/TP Zones
When enabled, the indicator draws colored boxes on the chart showing:
Red Zone: Stop Loss area - the region between your entry and stop loss
Green/Gold/Red Zone: Take Profit area - colored based on R:R quality
The color coding provides instant visual feedback:
Green: R:R meets or exceeds your "Good R:R" threshold (default 3:1)
Gold: R:R meets minimum threshold but below "Good" (between 2:1 and 3:1)
Red: R:R below minimum threshold - setup should be avoided
Swing Point Markers
Small circles mark detected swing points on the chart:
Green circles: Swing lows (potential support / long SL levels)
Red circles: Swing highs (potential resistance / short SL levels)
Dashboard Panel
The dashboard in the top-right corner displays comprehensive trade planning information:
R:R Row: Current Risk-to-Reward ratio for long and short setups
Status Row: VALID, OK, BAD, or N/A based on R:R thresholds
Stop Loss Row: Exact price level for stop loss placement
Take Profit Row: Exact price level for take profit placement
Pos Size Row: Recommended position size based on your risk parameters
Risk $ Row: Dollar amount at risk per trade
Position Sizing Logic
The indicator calculates position size using the formula:
Position Size = Risk Amount / Risk per Unit
Where:
Risk Amount = Account Size × (Risk Percentage / 100)
Risk per Unit = Entry Price - Stop Loss Price
For example, with a $10,000 account risking 1% per trade ($100), if your entry is at 100 and stop loss at 98 (risk of 2 per unit), your position size would be 50 units.
Input Parameters
Swing Detection:
Swing Lookback: Number of bars to look back for pivot detection (default: 10). Higher values find more significant swing points but may be slower to update.
Target Multiplier: Multiplier applied to risk to calculate take profit distance (default: 2). A value of 2 means TP is 2× the distance of SL from entry.
Risk/Reward Thresholds:
Minimum R:R: Minimum acceptable Risk-to-Reward ratio (default: 2.0). Setups below this show as "BAD" in red.
Good R:R: Threshold for excellent setups (default: 3.0). Setups at or above this show as "VALID" in green.
Account Settings:
Account Size ($): Your trading account size in dollars (default: 10,000). Used for position sizing calculations.
Risk Per Trade (%): Percentage of account to risk per trade (default: 1.0%). Professional traders typically risk 0.5-2% per trade.
Display:
Show SL/TP Zones: Toggle visibility of the colored zone boxes on chart (default: enabled)
Show Dashboard: Toggle visibility of the information panel (default: enabled)
Analyze Direction: Choose to analyze Long only, Short only, or Both directions (default: Both)
How to Use This Indicator
Basic Workflow:
Add the indicator to your chart
Configure your account size and risk percentage in the settings
Set your minimum and good R:R thresholds based on your trading rules
Look at the dashboard to see current R:R for potential long and short entries
Only consider trades where the status shows "VALID" or at minimum "OK"
Use the displayed SL and TP levels for your order placement
Use the position size recommendation to determine lot/contract size
Interpreting the Dashboard:
VALID (Green): Excellent setup - R:R meets your "Good" threshold. This is the ideal scenario for taking a trade.
OK (Gold): Acceptable setup - R:R meets minimum but isn't optimal. Consider taking if other confluence factors align.
BAD (Red): Poor setup - R:R below minimum threshold. Avoid this trade or wait for better entry.
N/A (Gray): Cannot calculate - usually means no valid swing point detected yet.
Best Practices:
Use this indicator as a filter, not a signal generator. It tells you IF a trade makes sense, not WHEN to enter.
Combine with your existing entry strategy - use Account Guardian to validate setups from other analysis.
Adjust the swing lookback based on your timeframe. Lower timeframes may need smaller lookback values.
Be honest with your account size input - accurate position sizing requires accurate inputs.
Consider the target multiplier carefully. Higher multipliers mean larger potential reward but lower probability of hitting TP.
Alerts
The indicator includes four alert conditions:
Good Long Setup: Triggers when long R:R reaches or exceeds your "Good R:R" threshold
Good Short Setup: Triggers when short R:R reaches or exceeds your "Good R:R" threshold
Bad Long Setup: Triggers when long R:R falls below your minimum threshold
Bad Short Setup: Triggers when short R:R falls below your minimum threshold
These alerts can help you monitor multiple charts and get notified when favorable setups appear.
Technical Implementation
The indicator is built using Pine Script v6 and includes:
Pivot-based swing detection using ta.pivothigh() and ta.pivotlow()
Dynamic box drawing for visual SL/TP zones
Table-based dashboard for clean information display
Color-coded visual feedback system
Persistent variable tracking for swing levels
Code Structure:
// Swing Detection
float swingHi = ta.pivothigh(high, swingLen, swingLen)
float swingLo = ta.pivotlow(low, swingLen, swingLen)
// R:R Calculation for Long
float longSL = recentSwingLo
float longRisk = entry - longSL
float longTP = entry + (longRisk * targetMult)
float longRR = (longTP - entry) / longRisk
// Position Sizing
float riskAmount = accountSize * (riskPct / 100)
float posSize = riskAmount / longRisk
Limitations
The indicator uses historical swing points which may not always represent optimal SL placement for your specific strategy
Position sizing assumes you can trade fractional units - adjust accordingly for instruments with minimum lot sizes
R:R calculations assume linear price movement and don't account for gaps or slippage
The indicator doesn't predict price direction - it only evaluates the mathematical viability of a setup
Swing detection has inherent lag due to the lookback period required for pivot confirmation
Recommended Settings by Trading Style
Scalping (1-5 minute charts):
Swing Lookback: 5-8
Target Multiplier: 1-2
Minimum R:R: 1.5
Good R:R: 2.0
Day Trading (15-60 minute charts):
Swing Lookback: 8-12
Target Multiplier: 2
Minimum R:R: 2.0
Good R:R: 3.0
Swing Trading (4H-Daily charts):
Swing Lookback: 10-20
Target Multiplier: 2-3
Minimum R:R: 2.5
Good R:R: 4.0
Why Risk/Reward Matters
Many traders focus solely on win rate, but profitability depends on the combination of win rate AND risk/reward ratio. Consider these scenarios:
50% win rate with 1:1 R:R = Breakeven (before costs)
50% win rate with 2:1 R:R = Profitable
40% win rate with 3:1 R:R = Profitable
60% win rate with 1:2 R:R = Losing money
Account Guardian helps ensure you only take trades where the math works in your favor, even if you're wrong more often than you're right.
Disclaimer
This indicator is provided for educational and informational purposes only. It is not intended as financial, investment, trading, or any other type of advice or recommendation.
Trading involves substantial risk of loss and is not suitable for all investors. The calculations provided by this indicator are based on historical price data and mathematical formulas that may not accurately predict future price movements.
Position sizing recommendations are estimates based on user inputs and should be verified before placing actual trades. Always consider factors such as leverage, margin requirements, and broker-specific rules when determining actual position sizes.
The Risk-to-Reward ratios displayed are theoretical calculations based on swing point detection. Actual trade outcomes will vary based on market conditions, execution quality, and other factors not captured by this indicator.
Past performance does not guarantee future results. Users should thoroughly test any trading approach in a demo environment before risking real capital. The authors and publishers of this indicator are not responsible for any losses or damages arising from its use.
Always consult with a qualified financial advisor before making investment decisions.
Pinescript
High-Probability Scalper (Market Open)Market open is where volatility is real, spreads are tight, and momentum shows itself early. This scalping strategy is built specifically to operate during that window, filtering out low-quality signals that usually appear later in the session.
Instead of trading all day, the logic is restricted to the first 90 minutes after market open, where continuation moves and fast pullbacks are more reliable.
What This Strategy Does
This script looks for short-term momentum alignment using:
Fast vs slow EMA structure
RSI confirmation to avoid chasing extremes
ATR-based risk control
Session-based filtering to trade only when volume matters
It’s designed for intraday scalping, not swing trading.
Core Trading Logic
1. Market Open Filter
Trades are allowed only between 09:30 – 11:00 exchange time.
This avoids low-liquidity chop and focuses on the period where most breakouts and reversals form.
2. Trend Confirmation
Bullish bias: 9 EMA crosses above 21 EMA
Bearish bias: 9 EMA crosses below 21 EMA
This keeps trades aligned with short-term direction instead of random entries.
3. Momentum Check (RSI)
RSI is used as a quality filter, not as an overbought/oversold signal.
Long trades only when RSI is strong but not extended
Short trades only when RSI shows weakness without exhaustion
This removes late entries and reduces whipsaws.
Entries & Exits
Entries
Executed only on confirmed candles
No intrabar repainting
One position at a time
Risk Management
Stop-loss based on ATR
Take-profit calculated using a fixed risk–reward ratio
Same structure for both long and short trades
This keeps risk consistent across different symbols and volatility levels.
Why This Strategy Works Better at Market Open
Volume is highest
False breakouts are fewer
EMA crosses have follow-through
RSI behaves more cleanly
By not trading all day, the strategy avoids most of the noise that kills scalpers.
Best Use Cases
Index futures
High-liquidity stocks
Major crypto pairs during active sessions
1m to 5m timeframes
What This Strategy Is NOT
Not a martingale
Not grid-based
Not designed for ranging markets
Not a “set and forget” system
It’s a controlled scalping template meant for disciplined execution.
How to Use It Properly
Test on multiple symbols
Adjust ATR length for volatility
Tune RSI ranges per market
Always forward-test before live alerts
Final Note
This strategy focuses on structure, timing, and risk, not indicator stacking.
If you trade the open, this gives you a clear framework instead of emotional entries.
If you want:
Alerts
Session customization
News filters
Partial exits
You can extend this logic without breaking the core system.
CryptoFlux Dynamo [JOAT]CryptoFlux Dynamo: Velocity Scalping Strategy
This Pine Script v6 strategy is designed for cryptocurrency markets operating on 5-minute and faster timeframes. It combines volatility regime detection, multi-path signal confirmation, and adaptive risk management to identify momentum-based trading opportunities in perpetual futures markets.
Core Design Principles
The strategy addresses three challenges specific to cryptocurrency trading:
24/7 market operation without session boundaries requires continuous monitoring and execution logic
Volatility regimes shift rapidly, demanding adaptive stop and target calculations
Tick-level responsiveness is critical for capturing momentum moves before they complete
Strategy Architecture
1. Signal Generation Stack
The strategy uses multiple technical indicators calibrated for cryptocurrency momentum:
MACD with parameters 8/21/5 (fast/slow/signal) optimized for crypto acceleration phases
EMA ribbon using 8/21/34 periods with slope analysis to assess trend structure
Volume impulse detection combining SMA baseline, standard deviation, and z-score filtering
RSI (21 period) and MFI (21 period) for momentum confirmation
Bollinger Bands and Keltner Channels for squeeze detection
2. Volatility Regime Classification
The strategy normalizes ATR as a percentage of price and classifies market conditions into three regimes:
Compression (< 0.8% ATR): Reduced position sizing, tighter stops (1.05x ATR), lower profit targets (1.6x ATR)
Expansion (0.8% - 1.6% ATR): Standard risk parameters, balanced risk-reward (1.55x stop, 2.05x target)
Velocity (> 1.6% ATR): Wider stops (2.1x ATR), amplified targets (2.8x ATR), tighter trailing offsets
ATR is calculated over 21 periods and smoothed with a 13-period EMA to reduce noise from wicks.
3. Multi-Path Entry System
Four independent signal pathways contribute to a composite strength score (0-100):
Trend Break (30 points): Requires EMA ribbon alignment, positive slope, and structure breakout above/below recent highs/lows
Momentum Surge (30 points): MACD histogram exceeds adaptive baseline, MACD line crosses signal, RSI/MFI above/below thresholds, with volume impulse confirmation
Squeeze Release (25 points): Bollinger Bands compress inside Keltner Channels, then release with momentum bias
Micro Pullback (15 points): Shallow retracements within trend structure that reset without breaking support/resistance
Additional scoring modifiers:
Volume impulse: +5 points when present, -5 when absent
Regime bonus: +5 in velocity, -2 in compression
Cycle bias: +5 when aligned, -5 when counter-trend
Trades only execute when the composite score reaches the minimum threshold (default: 55) and all filters agree.
4. Risk Management Framework
Position sizing is calculated from:
RiskCapital = Equity × (riskPerTradePct / 100)
StopDistance = ATR × StopMultiplier(regime)
Quantity = min(RiskCapital / StopDistance, MaxExposure / Price)
The strategy includes:
Risk per trade: 0.65% of equity (configurable)
Maximum exposure: 12% of equity (configurable)
Regime-adaptive stop and target multipliers
Adaptive trailing stops based on ATR and regime
Kill switch that disables new entries after 6.5% drawdown
Momentum fail-safe exits when MACD polarity flips or ribbon structure breaks
5. Additional Filters
Cycle Oscillator : Measures price deviation from 55-period EMA. Requires cycle bias alignment (default: ±0.15%) before entry
BTC Dominance Filter : Optional filter using CRYPTOCAP:BTC.D to reduce long entries during risk-off periods (rising dominance) and short entries during risk-on periods
Session Filter : Optional time-based restriction (disabled by default for 24/7 operation)
Strategy Parameters
All default values used in backtesting:
Core Controls
Enable Short Structure: true
Restrict to Session Window: false
Execution Session: 0000-2359:1234567 (24/7)
Allow Same-Bar Re-Entry: true
Optimization Constants
MACD Fast Length: 8
MACD Slow Length: 21
MACD Signal Length: 5
EMA Fast: 8
EMA Mid: 21
EMA Slow: 34
EMA Slope Lookback: 8
Structure Break Window: 9
Regime Intelligence
ATR Length: 21
Volatility Soothing: 13
Low Vol Regime Threshold: 0.8% ATR
High Vol Regime Threshold: 1.6% ATR
Cycle Bias Length: 55
Cycle Bias Threshold: 0.15%
BTC Dominance Feed: CRYPTOCAP:BTC.D
BTC Dominance Confirmation: true
Signal Pathways
Volume Baseline Length: 34
Volume Impulse Multiplier: 1.15
Volume Z-Score Threshold: 0.5
MACD Histogram Smoothing: 5
MACD Histogram Sensitivity: 1.15
RSI Length: 21
RSI Momentum Trigger: 55
MFI Length: 21
MFI Momentum Trigger: 55
Squeeze Length: 20
Bollinger Multiplier: 1.5
Keltner Multiplier: 1.8
Squeeze Release Momentum Gate: 1.0
Micro Pullback Depth: 7
Minimum Composite Signal Strength: 55
Risk Architecture
Risk Allocation per Trade: 0.65%
Max Exposure: 12% of Equity
Base Risk/Reward Anchor: 1.8
Stop Multiplier • Low Regime: 1.05
Stop Multiplier • Medium Regime: 1.55
Stop Multiplier • High Regime: 2.1
Take Profit Multiplier • Low Regime: 1.6
Take Profit Multiplier • Medium Regime: 2.05
Take Profit Multiplier • High Regime: 2.8
Adaptive Trailing Engine: true
Trailing Offset Multiplier: 0.9
Quantity Granularity: 0.001
Kill Switch Drawdown: 6.5%
Strategy Settings
Initial Capital: $100,000
Commission: 0.04% (0.04 commission_value)
Slippage: 1 tick
Pyramiding: 1 (no position stacking)
calc_on_every_tick: true
calc_on_order_fills: true
Visualization Features
The strategy includes:
EMA ribbon overlay (8/21/34) with customizable colors
Regime-tinted background (compression: indigo, expansion: purple, velocity: magenta)
Dynamic bar coloring based on signal strength divergence
Signal labels for entry points
On-chart dashboard displaying regime, ATR%, signal strength, position status, stops, targets, and risk metrics
Recommended Usage
Timeframes
The strategy is optimized for 5-minute charts. It can operate on 3-minute and 1-minute timeframes for faster scalping, or 15-minute for swing confirmation. When using higher timeframes, consider:
Increasing structure lookback windows
Raising RSI trigger thresholds above 58 to filter noise
Extending volume baseline length
Markets
Designed for high-liquidity cryptocurrency perpetual futures:
BTC/USDT, BTC/USD perpetuals
ETH perpetuals
Major L1 tokens with sufficient volume
For thinner order books, increase volume impulse multiplier and adjust quantity granularity to match exchange minimums.
Limitations and Compromises
Backtesting Considerations
TradingView strategy backtesting does not replicate broker execution. Actual fills, slippage, and commissions may differ
The strategy uses calc_on_every_tick=true and calc_on_order_fills=true to reduce bar-close distortions, but real execution still depends on broker infrastructure
At least 200 historical bars are required to stabilize regime classification, volume baselines, and cycle context
Market Structure Dependencies
BTC dominance feed ( CRYPTOCAP:BTC.D ) may lag during low-liquidity periods or weekends. Consider disabling the filter if data quality degrades
Volume impulse detection assumes consistent order book depth. During extreme volatility or exchange issues, volume signatures may be unreliable
Regime classification based on ATR percentage assumes normal volatility distributions. During black swan events, regime thresholds may not adapt quickly enough
Parameter Sensitivity
Default parameters are tuned for BTC/ETH perpetuals on 5-minute charts. Different assets or timeframes require recalibration
The composite signal strength threshold (55) balances selectivity vs. opportunity. Higher values reduce false signals but may miss valid setups
Risk per trade (0.65%) and max exposure (12%) are conservative defaults. Aggressive scaling increases drawdown risk
Execution Constraints
Same-bar re-entry requires broker support for rapid order placement
Quantity granularity must match exchange contract minimums
Kill switch drawdown (6.5%) may trigger during normal volatility cycles, requiring manual reset
Performance Expectations
This strategy is a framework for momentum-based cryptocurrency trading. Performance depends on:
Market conditions (trending vs. ranging)
Exchange execution quality
Parameter calibration for specific assets
Risk management discipline
Backtest results shown in publications reflect specific market conditions and parameter sets. Past performance does not indicate future results. Always forward test with paper trading or broker simulation before deploying live capital.
Code Structure
The strategy is organized into functional sections:
Configuration groups for parameter organization
Helper functions for position sizing and normalization
Core indicator calculations (MACD, EMA, ATR, RSI, MFI, volume analytics)
Regime classification logic
Multi-path signal generation and composite scoring
Entry/exit orchestration with risk management
Visualization layer with dashboard and chart elements
The source code is open and can be modified to suit your trading requirements. Everyone is encouraged to understand the logic before deploying and to test thoroughly in their target markets.
Modification Guidelines
When adapting this strategy:
Document any parameter changes in your publication
Test modifications across different market regimes
Validate position sizing logic for your exchange's contract specifications
Consider exchange-specific limitations (funding rates, liquidation mechanics, order types)
Conclusion
This strategy provides a structured approach to cryptocurrency momentum trading with regime awareness and adaptive risk controls. It is not a guaranteed profit system, but rather a framework that requires understanding, testing, and ongoing calibration to market conditions.
You should thoroughly understand the logic, test extensively in their target markets, and manage risk appropriately. The strategy's effectiveness depends on proper parameter tuning, reliable execution infrastructure, and disciplined risk management.
Disclaimer
This script and its documentation are for educational and informational purposes only. They do not constitute financial advice, investment recommendations, or trading advice of any kind. Trading cryptocurrencies and derivatives involves substantial risk of loss and is not suitable for all investors. Past performance, whether real or indicated by backtesting, does not guarantee future results.
This strategy is provided "as is" without any warranties or guarantees of profitability
You should not rely solely on this strategy for making trading decisions
Always conduct your own research and analysis before making any financial decisions
Consider consulting with a qualified financial advisor before engaging in trading activities
The authors and contributors are not responsible for any losses incurred from using this strategy
Cryptocurrency trading can result in the loss of your entire investment
Only trade with capital you can afford to lose
Use this strategy at your own risk. The responsibility for any trading decisions and their consequences lies entirely with you.
RSI Forecast [QuantAlgo]🟢 Overview
While standard RSI excels at measuring current momentum and identifying overbought or oversold conditions, it only reflects what has already happened in the market. The RSI Forecast indicator builds upon this foundation by projecting potential RSI trajectories into future bars, giving traders a framework to consider where momentum might head next. Three analytical models power these projections: a market structure approach that reads swing highs and lows, a volume analysis method that weighs accumulation and distribution patterns, and a linear regression model that extrapolates recent trend behavior. Each model processes market data differently, allowing traders to choose the approach that best fits their analytical style and the asset they're trading.
🟢 How It Works
At its foundation, the indicator calculates RSI using the standard methodology: comparing average upward price movements against average downward movements over a specified period, producing an oscillator that ranges from 0 to 100. Traders can apply an optional signal line using various moving average types (e.g., SMA, EMA, SMMA/RMA, WMA, or VWMA), and when SMA smoothing is selected, Bollinger Bands can be added to visualize RSI volatility ranges.
The forecasting mechanism operates by first estimating future price levels using the chosen projection method. These estimated prices then pass through a simulated RSI engine that mirrors the actual indicator's mathematics. The simulation updates the internal gain and loss averages bar by bar, applying the same RMA smoothing that powers real RSI calculations, to produce authentic projected values.
Since RSI characteristically moves in waves rather than straight lines, the projection system incorporates dynamic oscillation. This draws from stored patterns of recent RSI movements, factors in the tendency for RSI to pull back from extreme readings, and applies mathematical wave functions tied to current momentum conditions. The Oscillation Intensity control lets traders adjust how much waviness appears in projections. Signal line (RSI-based MA) projections follow the same logic, advancing the chosen moving average type forward using its proper mathematical formula. The complete system generates 15 bars of projected RSI and signal line values, displayed as dashed lines extending beyond current price action.
🟢 Key Features
1. Market Structure Model
This projection method reads price action through swing point analysis. It scans for pivot highs and pivot lows within a defined lookback range, then evaluates whether the market is building bullish patterns (successive higher highs and higher lows) or bearish patterns (successive lower highs and lower lows). The algorithm recognizes structural shifts when price violates previous swing levels in either direction.
Price projections under this model factor in proximity to key swing levels and overall trend strength, measured by tallying trend-confirming swings over recent history. When bullish structure prevails and price hovers near support, upward price bias enters the projection, pushing forecasted RSI higher. Bearish structure near resistance creates the opposite effect. The model scales its projections using ATR to keep them proportional to current volatility conditions.
▶ Practical Implications for Traders:
Aligns well with traders who focus on support, resistance, and swing-based entries
Provides context for where RSI might travel as price interacts with structural levels
Tends to perform better when markets display clear directional swings
May produce less useful output during consolidation phases with overlapping swings
Offers early visualization of potential divergence setups
Swing traders can use structure-based projections to time entries around key pivot zones
Position traders could benefit from the trend strength component when holding through larger moves
On lower timeframes, it helps scalpers identify micro-structure shifts for quick momentum plays
Useful for mapping out potential RSI behavior around breakout and breakdown levels
Day traders can combine structural projections with session highs and lows for intraday context
2. Volume-Weighted Model
This method blends multiple volume indicators to inform its price projections. It tracks On-Balance Volume to gauge cumulative buying and selling pressure, monitors the Accumulation/Distribution Line to assess where price closes relative to its range on each bar, and calculates volume-weighted returns to give heavier influence to high-volume price movements. The model examines the directional slope of these metrics to assess whether volume confirms or contradicts price direction.
Unusually high volume bars receive special attention, with their directional bias factored into projections. When all volume metrics point the same direction, the model produces more aggressive price forecasts and consequently stronger RSI movements. Conflicting volume signals lead to more muted projections, suggesting RSI may move sideways rather than trending.
▶ Practical Implications for Traders:
Suited for traders who incorporate volume confirmation into their analysis
Works best with instruments that report accurate, meaningful volume data
Useful for identifying situations where momentum lacks volume support
Less applicable to instruments with sparse or unreliable volume information
Scalpers on liquid markets can spot volume-backed momentum for quick entries and exits
Helps intraday traders distinguish between genuine moves and low-volume fakeouts
Position traders can assess whether institutional participation supports longer-term trends
Effective during news events or market opens when volume spikes often drive directional moves
Swing traders can use volume divergence in projections to anticipate potential reversals
3. Linear Regression Model
The simplest of the three methods, linear regression fits a straight line through recent price data using least-squares mathematics and extends that line forward. These projected prices then generate corresponding RSI forecasts. This creates a clean momentum projection without conditional logic or interpretation of market characteristics. The forecast simply asks: if the recent price trend continues at its current rate of change, where would RSI be in the coming bars?
▶ Practical Implications for Traders:
Delivers a clean, mathematically neutral projection baseline
Functions well during sustained, orderly trends
Involves fewer parameters and produces consistent, reproducible output
Responds more slowly when trend direction shifts
Works best in trending environments rather than ranging markets
Ideal for position traders who want to ride established trends
Useful for swing traders to gauge trend exhaustion when actual RSI deviates from linear projections
Scalpers can use the smooth output as a reference point to measure short-term momentum deviations
Effective baseline for comparing against structure or volume models to measure market complexity
Works particularly well on higher timeframes where trends develop more gradually
🟢 Universal Applications Across All Models
Regardless of which forecasting method you select, the indicator projects future RSI positions that may help with:
▶ Overbought/Oversold Planning: See whether RSI trajectories point toward extreme zones, giving you time to prepare responses before conditions develop
▶ Entry and Exit Timing: Factor projected RSI levels into your timing decisions for opening or closing positions
▶ Crossover Anticipation: Watch for projected crossings between RSI and its signal line (RSI-based MA) that might indicate upcoming momentum shifts
▶ Mean Reversion Context: When RSI sits at extremes, projections can illustrate potential paths back toward the midline
▶ Momentum Evaluation: Assess whether current directional strength appears likely to continue or fade based on projection direction
▶ Divergence Awareness: Use forecast trajectories alongside price action to spot potential divergence formations earlier
▶ Comparative Analysis: Run different projection methods and note where they agree or disagree, using alignment as an additional filter, for instance
▶ Multi-Timeframe Context: Compare RSI projections across different timeframes to identify alignment or conflict in momentum outlook
▶ Trade Management: Reference projected RSI levels when adjusting stops, scaling positions, or setting profit targets
▶ Rule-Based Systems: Incorporate projected RSI conditions into systematic trading approaches for more forward-looking signal generation
Note: It is essential to recognize that these forecasts derive from mathematical analysis of recent price behavior. Markets are dynamic environments shaped by innumerable factors that no technical tool can fully capture or foresee. The projected RSI values represent potential scenarios for how momentum might develop, and actual readings can take different paths than those visualized. Historical tendencies and past patterns offer no guarantee of future behavior. Consider these projections as one element within a comprehensive trading approach that encompasses disciplined risk management, appropriate position sizing, and diverse analytical methods. The true benefit lies not in expecting precise forecasts but in developing a forward-thinking perspective on possible market conditions and planning your responses accordingly.
Volume Profile Lite [JOAT]
Volume Profile Lite — Simplified Volume-at-Price Analysis
Volume Profile Lite creates a histogram showing volume distribution across price levels using a proprietary lightweight calculation method. It identifies the Point of Control (POC), Value Area High, and Value Area Low—key concepts from auction market theory—in an optimized, easy-to-read format that won't slow down your charts.
Why This Script is Protected
This script is published as closed-source to protect the proprietary volume distribution algorithm and the optimized Value Area calculation methodology from unauthorized republishing. The specific implementation of volume allocation across price rows, the buy/sell volume separation logic, and the efficient POC detection system represents original work that provides a unique lightweight alternative to standard volume profile implementations.
What Makes This Indicator Unique
Unlike heavy volume profile indicators that can slow down charts, Volume Profile Lite:
Uses an optimized algorithm designed for performance
Separates buying and selling volume for additional insight
Provides clean visual presentation without chart clutter
Includes extending reference lines for key levels
Features a dashboard with price position relative to POC
What This Indicator Does
Distributes volume across price rows to create a visual profile histogram
Identifies the Point of Control (highest volume price level)
Calculates Value Area (where specified percentage of volume traded)
Separates buying and selling volume for each price level
Extends key levels as reference lines on the chart
Highlights the POC row with a distinct border
Core Methodology
The indicator uses a proprietary approach to volume-at-price analysis:
Price Row Division — The lookback range is divided into configurable price rows (default: 24 rows)
Volume Distribution — Each bar's volume is allocated to the price rows it touches. If a bar spans multiple rows, volume is distributed proportionally.
Buy/Sell Separation — Volume is classified based on bar direction (close >= open = buying volume, close < open = selling volume)
POC Detection — The row with maximum accumulated volume is identified as the Point of Control
Value Area Calculation — Starting from POC, expands outward (alternating up and down) until target volume percentage is captured
Key Concepts Explained
Point of Control (POC) — The price level with the highest volume concentration. Often acts as a magnet for price and represents "fair value" for the analyzed period. Price tends to return to POC.
Value Area High (VAH) — Upper boundary of the value area zone. Acts as resistance when price is below, support when price is above.
Value Area Low (VAL) — Lower boundary of the value area zone. Acts as support when price is above, resistance when price is below.
Value Area — Price range containing specified percentage (default 70%) of total volume. This is where most trading activity occurred.
Visual Features
Volume Histogram — Horizontal bars showing volume at each price level
Buy/Sell Coloring — Green portions show buying volume, red shows selling volume
POC Highlight — The POC row has a distinct orange border and fill
POC Line — Horizontal line extending from POC (optional extension to right)
Value Area Lines — Dashed blue lines at VAH and VAL
Value Area Fill — Subtle blue fill between VAH and VAL
Color Scheme
Up Volume Color — Default: #26A69A (teal) — Buying volume
Down Volume Color — Default: #EF5350 (red) — Selling volume
POC Color — Default: #FF9800 (orange) — Point of Control
Value Area Color — Default: #2196F3 (blue) — VAH/VAL lines and fill
Dashboard Information
The on-chart table (bottom-right corner) displays:
POC price level
Value Area High price level
Value Area Low price level
Current price position relative to POC (ABOVE POC, BELOW POC, or AT POC)
Distance from current price to POC as percentage
Inputs Overview
Calculation Settings:
Lookback Period — Number of bars to analyze (default: 100, range: 20-500)
Number of Rows — Price level divisions for the profile (default: 24, range: 10-50)
Value Area % — Percentage of volume for value area calculation (default: 70%, range: 50-90%)
Visual Settings:
Up/Down Volume Colors — Customizable buy/sell colors
POC Color — Point of Control highlighting
Value Area Color — VAH/VAL line and fill color
Profile Width — Visual width of histogram in bars (default: 30, range: 10-100)
Show POC Line — Toggle POC horizontal line
Show Value Area — Toggle VAH/VAL lines and fill
Show Dashboard — Toggle the information table
Extend Lines — Project POC and VA lines further right
How to Use It
For Support/Resistance:
Use POC as a potential support/resistance reference point
Price often gravitates back to POC (mean reversion)
VAH acts as resistance when approaching from below
VAL acts as support when approaching from above
For Trend Analysis:
Price above POC suggests bullish control
Price below POC suggests bearish control
Breaking out of Value Area often leads to trending moves
Returning to Value Area suggests failed breakout
For Entry/Exit:
Enter longs near VAL with stops below
Enter shorts near VAH with stops above
Target POC for mean-reversion trades
Use POC as a trailing stop reference in trends
Alerts Available
VPL Cross Above POC — Price crosses above Point of Control
VPL Cross Below POC — Price crosses below Point of Control
VPL Cross Above VAH — Price breaks above Value Area High
VPL Cross Below VAL — Price breaks below Value Area Low
Best Practices
Use longer lookback periods for more significant levels
Increase row count for more precise level identification
POC from higher timeframes is more significant
Combine with other indicators for confirmation
This indicator is provided for educational purposes. It does not constitute financial advice. Past performance does not guarantee future results. Always conduct your own analysis and use proper risk management before making trading decisions.
— Made with passion by officialjackofalltrades
RegimeLens [JOAT]RegimeLens — Market Regime Detection and Classification
RegimeLens identifies whether the market is in a Trending, Ranging, or Volatile state using a proprietary combination of trend strength analysis, volatility measurement, and percentile-based classification. Understanding the current market regime helps traders adapt their approach to current conditions—because the strategy that works in a trend will fail in a range.
Why This Script is Protected
This script is published as closed-source to protect the proprietary regime classification algorithm and the specific threshold calibration methodology from unauthorized republishing. The unique combination of ADX analysis, Bollinger Band width percentiles, ATR percentile ranking, and the transition zone logic represents original work that goes beyond standard regime detection approaches.
What Makes This Indicator Unique
Unlike simple trend indicators, RegimeLens:
Classifies markets into four distinct regimes, not just "trending" or "not trending"
Uses percentile-based volatility analysis for more adaptive classification
Includes a transition zone logic to prevent rapid regime flip-flopping
Tracks regime duration and strength for additional context
Provides visual regime changes with on-chart labels
What This Indicator Does
Classifies market into four regimes: Trend Up, Trend Down, Ranging, or Volatile
Displays Bollinger Bands colored according to current regime
Marks regime changes with on-chart labels
Colors price bars according to detected regime
Tracks regime duration and strength metrics
Provides comprehensive dashboard with all regime metrics
Core Methodology
The indicator analyzes multiple market dimensions to determine the current regime:
Trend Strength Analysis (ADX) — Measures directional movement strength regardless of direction. High ADX indicates trending; low ADX indicates ranging.
Directional Bias (DI+ vs DI-) — Determines whether bullish or bearish forces dominate when a trend is detected.
Volatility Expansion/Contraction (BB Width) — Tracks Bollinger Band width relative to historical norms using percentile ranking.
ATR Percentile Ranking — Compares current ATR to its historical distribution to identify abnormally high volatility conditions.
Regime Definitions
Trend Up (Green) — ADX above trending threshold with DI+ > DI- and price above basis. Strong directional movement with bullish bias confirmed.
Trend Down (Red) — ADX above trending threshold with DI- > DI+ and price below basis. Strong directional movement with bearish bias confirmed.
Ranging (Yellow) — ADX below ranging threshold indicating sideways consolidation. Low directional strength suggests mean-reversion strategies may work better.
Volatile (Purple) — Both ATR percentile AND BB width percentile above the high volatility threshold. Indicates unstable, potentially dangerous conditions where normal strategies may fail.
The classification uses a priority system where high volatility conditions take precedence, followed by trend strength evaluation, with ranging as the default state for low-activity periods.
Regime Strength Calculation
Each regime has an associated strength score (0-100%) that indicates how firmly the market is in that state:
For trends: Based on ADX relative to threshold plus BB percentile
For ranging: Based on inverse ADX plus inverse BB percentile
For volatile: Based on ATR percentile
This helps identify when regime transitions may be approaching—declining strength often precedes regime changes.
Visual Features
Regime-Colored Bollinger Bands — Upper, basis, and lower bands all colored by current regime
Band Fill — 85% transparent fill between bands in regime color
Background Highlighting — Optional 90% transparent background in regime color
Regime Change Labels — On-chart markers when regime changes (arrows for trends, diamond for range, X for volatile)
Bar Coloring — Optional price bar coloring by regime
Color Scheme
Trend Up Color — Default: #00C853 (bright green)
Trend Down Color — Default: #FF1744 (bright red)
Range Color — Default: #FFD600 (yellow)
Volatile Color — Default: #AA00FF (purple)
Dashboard Information
The on-chart table (top-right corner) displays:
Current regime name with color coding
ADX value (highlighted if above trend threshold)
DI+ / DI- comparison with directional coloring
Bollinger Band width percentage
Volatility percentile (highlighted if above volatile threshold)
Regime strength percentage
Duration in bars since last regime change
Inputs Overview
Detection Settings:
ADX Length — Period for ADX/DI calculation (default: 14, range: 5-50)
BB Length — Period for Bollinger Bands (default: 20, range: 10-100)
BB Multiplier — Standard deviation multiplier (default: 2.0, range: 1.0-4.0)
ATR Length — Period for ATR calculation (default: 14, range: 5-50)
Thresholds:
Trending ADX Threshold — ADX level above which market is considered trending (default: 25, range: 15-50)
Ranging ADX Threshold — ADX level below which market is considered ranging (default: 20, range: 10-40)
High Volatility Percentile — Percentile above which volatile regime is triggered (default: 75, range: 50-95)
Visual Settings:
Trend Up/Down/Range/Volatile Colors — Fully customizable color scheme
Show Background — Toggle regime-colored background
Show Regime Bands — Toggle Bollinger Bands display
Show Dashboard — Toggle the information table
Color Price Bars — Toggle bar coloring by regime
How to Use It
Strategy Selection:
Trend Up/Down — Use trend-following strategies (breakouts, pullbacks, moving average systems)
Ranging — Use mean-reversion strategies (support/resistance bounces, oscillator extremes)
Volatile — Reduce position size, widen stops, or stay flat until conditions stabilize
For Regime Change Trading:
Watch for regime change labels as potential entry points
Trend regime starting often signals breakout opportunity
Ranging regime starting after trend may signal consolidation before continuation
Volatile regime is a warning to be cautious
For Risk Management:
Increase position size during strong trend regimes
Decrease position size during volatile or ranging regimes
Use regime strength to gauge conviction
Monitor duration—very long regimes may be due for change
Alerts Available
MRD Trend Up — Market regime changed to trending bullish
MRD Trend Down — Market regime changed to trending bearish
MRD Ranging — Market regime changed to sideways consolidation
MRD Volatile — Market regime changed to high volatility state
MRD Any Change — Notification on any regime transition
Best Practices
Don't fight the regime—adapt your strategy to current conditions
Volatile regime is a warning sign, not a trading signal
Use regime strength to gauge how established the current state is
Combine with other indicators appropriate for the detected regime
This indicator is provided for educational purposes. It does not constitute financial advice. Past performance does not guarantee future results. Always conduct your own analysis and use proper risk management before making trading decisions.
— Made with passion by officialjackofalltrades
Fractal Support & Resistance [JOAT]
Fractal Support & Resistance — Automatic Level Detection with Volume Weighting
Fractal Support & Resistance automatically identifies key price levels using a proprietary combination of fractal detection, volume analysis, and dynamic touch counting. Levels are intelligently styled based on their strength and how many times they have been tested, giving you instant visual feedback on level importance.
Why This Script is Protected
This script is published as closed-source to protect the proprietary level management algorithm and the unique volume-weighted strength calculation methodology from unauthorized republishing. The specific implementation of touch detection, level merging logic, and dynamic opacity calculations represents original work that differentiates this from standard fractal indicators.
What Makes This Indicator Unique
Unlike basic fractal indicators that simply mark pivot points, this system:
Tracks how many times each level has been tested (touch counting)
Weights level importance by volume at the fractal point
Merges nearby fractals into single levels instead of cluttering the chart
Dynamically adjusts visual opacity based on level strength
Provides zone boxes around levels for realistic price reaction areas
What This Indicator Does
Detects fractal pivot highs and lows to establish support and resistance levels
Tracks how many times each level has been touched or tested
Weights level importance by volume at the fractal point
Draws extending lines and zone boxes for each level
Dynamically adjusts level opacity based on touch count for visual strength indication
Provides a dashboard with nearest levels and counts
Core Methodology
The indicator uses Williams Fractal concepts as a foundation but extends them with proprietary enhancements:
Fractal Detection — Identifies pivot highs and lows where price creates local extremes with confirmation bars on each side. A fractal high requires the highest point with lower highs on both sides; a fractal low requires the lowest point with higher lows on both sides.
Level Clustering — New fractals within a tolerance zone (based on Zone Padding %) update existing levels rather than creating duplicates. This keeps the chart clean and focuses on significant price areas.
Volume Integration — Volume at each fractal point is accumulated to weight level significance. Higher volume fractals are considered more important.
Touch Tracking — The system monitors when price approaches existing levels and increments touch counts. More touches indicate stronger, more significant levels.
Visual Strength System
Level appearance changes dynamically based on market interaction:
Newer or less-tested levels appear more transparent (up to 80% transparency)
Each additional touch reduces transparency by 15%
Heavily tested levels become more prominent and opaque (minimum 20% transparency)
Labels display level number and touch count (e.g., "R1 (3)" = Resistance 1 with 3 touches)
Zone boxes provide visual areas around each level
Color Scheme
Resistance Color — Default: #FF5252 (red) — Used for resistance levels and zones
Support Color — Default: #4CAF50 (green) — Used for support levels and zones
Zone Fill — 90% transparent version of level color
Zone Border — 70% transparent version of level color
Labels — 30% transparent background with white text
Dashboard Information
The on-chart table (bottom-left corner) displays:
Number of active resistance levels meeting minimum touch requirement
Number of active support levels meeting minimum touch requirement
Nearest resistance level above current price
Nearest support level below current price
Inputs Overview
Fractal Settings:
Fractal Period — Bars on each side for fractal confirmation (default: 2, range: 1-10)
Max Levels Per Side — Maximum resistance and support levels to track (default: 5, range: 1-20)
Zone Padding (%) — Level zone width as percentage of price (default: 0.2%, range: 0-2%)
Filtering:
Volume Weight Levels — Toggle volume-weighted level importance (default: on)
Min Touches to Show — Filter out levels with fewer touches (default: 1, range: 1-10)
Lookback Period — Historical bars to analyze for level detection (default: 200, range: 50-500)
Visual Settings:
Resistance/Support Colors — Customizable color scheme
Show Zone Boxes — Toggle filled zone areas around levels
Show Level Labels — Toggle level labels with touch counts
Show Fractal Markers — Toggle small triangles at fractal points
Show Dashboard — Toggle the information table
Line Width — Thickness of level lines (default: 2, range: 1-5)
How to Use It
For Support/Resistance Trading:
Use levels with higher touch counts as stronger support/resistance references
More opaque levels have been tested more times and are more significant
Watch for price reactions at zone boundaries, not just exact level prices
Combine with candlestick patterns at levels for entry signals
For Breakout Trading:
Watch for breakouts when price closes beyond a level
Levels with many touches that finally break often produce strong moves
Use the zone box—a close beyond the zone is more significant than just touching the level
Set alerts for resistance/support breaks
For Target Setting:
Use the nearest resistance as a profit target for long positions
Use the nearest support as a profit target for short positions
Dashboard shows these levels for quick reference
Alerts Available
FSR Resistance Break — Price closes above a resistance level
FSR Support Break — Price closes below a support level
FSR New Fractal High — Fresh fractal high detected
FSR New Fractal Low — Fresh fractal low detected
Best Practices
Increase Fractal Period for fewer but more significant levels
Use Min Touches filter to show only well-tested levels
Volume weighting helps identify institutionally significant levels
Combine with trend indicators—trade with the trend at levels
This indicator is provided for educational purposes. It does not constitute financial advice. Past performance does not guarantee future results. Always conduct your own analysis and use proper risk management before making trading decisions.
— Made with passion by officialjackofalltrades
Neural Trend Engine [JOAT]Neural Trend Engine - Multi-Layer Adaptive Trend Detection
Neural Trend Engine uses a multi-layer filtering approach inspired by neural network concepts. It combines multiple adaptive moving averages with proprietary momentum and volatility weighting to generate trend signals with reduced lag and improved confidence scoring.
Why This Script is Protected
This script is published as closed-source to protect the proprietary signal composition algorithm and the specific weighting methodology from unauthorized republishing. The unique combination of adaptive layer calculations, momentum normalization, and volatility integration represents original work that goes beyond standard indicator implementations.
What Makes This Indicator Unique
Unlike simple moving average crossover systems, Neural Trend Engine:
Uses three Kaufman Adaptive Moving Averages (KAMA) that automatically adjust their smoothing based on market efficiency
Combines layer alignment, momentum, and volatility into a single "neural signal"
Provides signal strength percentages so you know the conviction level of each signal
Creates a visual trend cloud that makes direction immediately obvious
What This Indicator Does
Plots three adaptive moving average "layers" that respond dynamically to market efficiency
Creates a trend cloud between fast and slow layers for visual trend identification
Generates weighted composite signals from layer alignment, momentum, and volatility
Displays buy/sell labels with signal strength percentages
Provides a comprehensive dashboard with multi-component breakdown
Colors the neural line and cloud based on current trend direction
Core Methodology
The indicator employs a three-layer adaptive system where each layer responds to market conditions at different speeds:
Fast Layer (default: 8) — Quick response for short-term direction changes
Medium Layer (default: 21) — Intermediate trend reference
Slow Layer (default: 55) — Long-term trend anchor
Each layer uses efficiency-based adaptation, meaning they become more responsive during trending conditions and smoother during choppy markets.
The neural signal is a proprietary composite that weighs three distinct market components:
Momentum Component (default: 40%) — Measures directional price velocity, normalized to its recent range
Trend Component (default: 35%) — Evaluates alignment between the three adaptive layers
Volatility Component (default: 25%) — Incorporates market volatility state into signal generation
These components are combined using a weighted formula that has been calibrated to balance responsiveness with noise reduction.
Signal Generation
Direction changes occur when the smoothed neural signal crosses a configurable strength threshold:
Bullish — Signal exceeds positive threshold with layer alignment confirmation
Bearish — Signal drops below negative threshold with layer alignment confirmation
Neutral — Signal remains within threshold range, indicating consolidation
Signal strength percentages indicate the conviction level of each signal, helping traders assess trade quality. Higher percentages suggest stronger trend conviction.
Visual Features
Trend Cloud — Filled area between fast and slow layers, colored by trend direction
Neural Line with Glow — Weighted average of all three layers with glow effect
Medium Layer — Subtle white line showing intermediate trend
Signal Labels — BUY/SELL labels with strength percentages at signal points
Small Markers — Alternative triangle markers when labels are disabled
Color Scheme
Bullish Color — Default: #26A69A (teal green) — Used for bullish trends and signals
Bearish Color — Default: #EF5350 (red) — Used for bearish trends and signals
Cloud Fill — 85% transparent version of trend color
Neural Line Glow — 60% transparent version for glow effect
Dashboard Information
The on-chart table (top-right corner) displays:
Current direction (BULLISH, BEARISH, or NEUTRAL)
Neural signal percentage
Layer alignment status (ALIGNED UP, ALIGNED DOWN, or MIXED)
Momentum direction and percentage
Trend strength percentage
Inputs Overview
Neural Layers:
Fast Layer — Period for fast adaptive MA (default: 8, range: 2-50)
Medium Layer — Period for medium adaptive MA (default: 21, range: 5-100)
Slow Layer — Period for slow adaptive MA (default: 55, range: 10-200)
Source — Price source for calculations (default: close)
Sensitivity:
Momentum Weight — Weight for momentum component (default: 0.4)
Trend Weight — Weight for trend/layer alignment (default: 0.35)
Volatility Weight — Weight for volatility component (default: 0.25)
ATR Period — Period for volatility calculations (default: 14)
Visual Settings:
Bullish/Bearish Colors — Customizable color scheme
Show Trend Cloud — Toggle the filled cloud area
Show Signal Labels — Toggle BUY/SELL labels with percentages
Show Neural Line — Toggle the main trend line
Show Dashboard — Toggle the information table
Alerts:
Await Bar Confirmation — Wait for bar close before triggering (recommended)
Min Signal Strength — Threshold for direction changes (default: 0.3 = 30%)
How to Use It
For Trend Following:
Follow the trend cloud color for overall market direction
Enter long when cloud turns bullish (teal) and signal strength is high
Enter short when cloud turns bearish (red) and signal strength is high
Use the neural line as a trailing stop reference
For Signal Trading:
Wait for BUY/SELL labels to appear
Check the signal strength percentage—higher is better
Confirm with dashboard showing aligned layers
Avoid signals during MIXED layer alignment
For Confirmation:
Use Neural Trend Engine to confirm signals from other systems
Strong confirmation when all three layers are aligned
Dashboard shows momentum and trend strength for additional context
Alerts Available
NTE Buy Signal — Bullish direction change detected
NTE Sell Signal — Bearish direction change detected
NTE Direction Change — Any trend direction change
Best Practices
Higher signal strength percentages indicate more reliable signals
Wait for layer alignment (shown in dashboard) before entering trades
Use on higher timeframes for more reliable trend identification
Combine with support/resistance levels for entry timing
This indicator is provided for educational purposes. It does not constitute financial advice. Past performance does not guarantee future results. Always conduct your own analysis and use proper risk management before making trading decisions.
— Made with passion by officialjackofalltrades
XAU Seasonality + Setup Quality + Month Strength | WarRoomXYZXAU Seasonality Engine is a technical analysis indicator developed for the study of recurring, calendar-based behavior on XAUUSD (Gold).
The tool blends month-of-year seasonality statistics with higher-timeframe context and a setup-quality gate to help users observe when market conditions historically lean strong, weak, or neutral — and how strict trade selection should be during each regime.
Indicator Concept
An indicator for XAUUSD that combines:
1. Seasonality Regime (Month-of-Year Bias)
► Classifies the current month as Strong / Weak / Neutral based on either:
• Preset months (user-defined)
or
• Auto mode (computed from historical monthly performance)
► Strong months suggest a bullish tailwind (not a signal).
► Weak months suggest headwind / caution and require stricter setup quality.
2. Monthly Performance Engine (Under the Hood)
► Uses the symbol’s monthly timeframe data to compute, per calendar month:
• Average monthly return (%)
• Win rate (%) — how often that month closes positive
• Month Strength Score (0–100) — a blended score derived from performance data
► The score is designed to provide a relative strength snapshot of seasonality by month.
3. Month Strength Histogram
► Plots a histogram (0–100) of the current month’s strength score.
• Higher bars = historically stronger month tendency
• Lower bars = historically weaker month tendency
► Optional horizontal reference lines mark “strong” and “weak” zones to make regimes obvious at a glance.
4. Setup Quality Meter (Confluence Filter)
► The indicator calculates a Setup Quality Score (0–100) using market structure and momentum components, such as:
• EMA trend alignment
• Momentum confirmation (EMA fast vs slow)
• Structure break confirmation (BOS)
• Liquidity sweep behavior
• Candle confirmation logic
► This score is intended as a trade-selectivity filter , not a trade executor.
5. Adaptive Rules for Weak Months (Strict Mode)
► When the indicator detects a weak seasonal regime, conditions automatically tighten:
• The A+ threshold increases (adaptive thresholding)
• Optional rule: Weak months require BOS + Sweep + FVG simultaneously before any A+ condition is considered valid
This forces the user into “higher-quality-only” behavior during historically weaker seasonal periods.
🔹1 Visual Components Included
• Seasonality regime label (Strong / Weak / Neutral)
• Optional background shading based on regime
• Month Strength Score histogram (0–100)
• Current month stats: Avg return + win rate
• Setup Quality Meter value (0–100)
• Adaptive A+ threshold display
• Weak-month confluence gate status (BOS / Sweep / FVG pass/fail)
• Optional alerts when strict criteria are met
➣What Means in the XAU Indicator
🔹 Definition (in THIS indicator)
Win Rate = the percentage of historical months that closed positive for the same calendar month.
It is NOT:
trade win rate ❌
signal accuracy ❌
It is a s tatistical seasonality metric .
How It’s Calculated
For each calendar month (January, February, etc.), the indicator:
1.Looks at historical monthly candles (Monthly timeframe).
2. Counts how many times that month:
•Closed higher than it opened (or higher than previous month close).
3. Divides:
Number of positive months
÷
Total number of observed months
× 100
Example: September
If over the last 20 years:
September closed green 14 times
September closed red 6 times
Then:
Win Rate = (14 / 20) × 100 = 70%
That’s what you see as in the dashboard.
What the Win Rate Is Used For
1️⃣ Part of the Month Strength Score
The indicator blends:
•Average Monthly Return (%) → measures magnitude
•Win Rate (%) → measures consistency
Combined into:
Month Strength Score (0–100)
This avoids a common trap:
•A month with 1 huge rally but many losses ≠ reliable
•A month with steady positive closes = higher quality environment
What Win Rate Tells You
High Win Rate (e.g. 65–75%)
•Gold more often closes higher in this month
•Continuation is statistically more likely
•Pullbacks are more likely to resolve in trend direction
Low Win Rate (e.g. 35–45%)
•Gold more often fails to close higher
•More chop, deeper retracements, false breakouts
•Continuation trades statistically struggle
What It Does NOT Tell You
🚫 It does NOT mean:
•“You will win 70% of your trades”
•“Every setup in this month works”
•“Direction is guaranteed”
Seasonality is context, not prediction.
Why This Is Powerful When Combined With Your System
On its own, win rate is just data.
But in your indicator, it’s used to:
•🔒 Raise the A+ threshold in weak months
•🧠 Force BOS + Sweep + FVG confluence
•❌ Block marginal setups automatically
So instead of guessing:
-“Why is gold so choppy this month?”
You know:
-“This month historically underperforms SO I must be stricter.”
➣What Means in the XAU Seasonality Indicator
🔹 Definition (in THIS indicator)
Avg Monthly Return = the average percentage gain or loss of XAUUSD for a specific calendar month, calculated across many years.
It measures magnitude , not frequency.
It is NOT:
•trade profit ❌
•expected return for the next month ❌
•guaranteed performance ❌
It is a historical seasonality tendency.
How It’s Calculated
For each calendar month (January, February, etc.), the indicator:
1.Takes every historical occurrence of that month.
2.Calculates the percentage change of the monthly candle:
(Monthly Close − Previous Monthly Close)
÷ Previous Monthly Close × 100
3. Adds all those percentage changes together.
4. Divides by the total number of observations.
Example: September
Assume over 20 years:
+2.4%, +1.1%, −0.6%, +3.0%, +1.8%, ...
If the sum of all September returns = +28% across 20 years:
Avg Monthly Return = +1.40%
That’s the number displayed in the indicator.
What Avg Monthly Return Is Used For
1️⃣ Measuring Strength of Movement
•Win Rate → “How often does it close green?”
•Avg Monthly Return → “How big are the moves when it works?”
Both are needed.
A month can:
•Win often but move very little
•Move a lot but only occasionally
The indicator combines both to avoid misleading conclusions.
How to Interpret Avg Monthly Return
Positive Avg Return (e.g. +0.8% to +2.0%)
•Gold tends to expand during this month
•Continuation phases are more likely
•Pullbacks are often absorbed
Near-Zero Avg Return (e.g. −0.2% to +0.2%)
•Market is statistically balanced
•Expect chop, rotations, false breaks
•Continuation is less reliable
Negative Avg Return (e.g. −0.5% or worse)
•Downward pressure or heavy mean reversion
•Rallies often fade
•Risk of aggressive stop hunts
What Avg Monthly Return Does NOT Mean
🚫 It does NOT mean:
•“Price will move +1.4% this month”
•“You should buy because the number is positive”
•“This is a guaranteed edge”
It describes historical behavior, not future certainty.
Why Avg Monthly Return Matters More Than People Think
Two months can have the same win rate but behave very differently:
Example:
Month Win Rate Avg Return Reality
Month A 65% +0.2% Small, choppy wins
Month B 55% +1.6% Fewer wins, but strong expansions
Your indicator would rank Month B as stronger, which is correct for continuation-based strategies.
How It Feeds the Month Strength Score
The indicator blends:
•60% Avg Monthly Return (normalized)
•40% Win Rate
This means:
•Big moves matter more than small consistency
•But consistency still matters enough to prevent distortion
Result:
Month Strength Score (0–100)
Which is then used to:
•tighten or relax A+ thresholds
•activate weak-month strict rules
•control trade frequency
🔹2. Intended Use
The indicator is designed as a discretionary analysis tool to support study of:
• seasonal bias and calendar tendencies
• relative strength/weakness across months
• how strict trade selection should be across different regimes
• confluence behavior when seasonal conditions are unfavorable
The tool does not generate forecasts, does not guarantee outcomes, and should not be relied upon as a stand-alone decision mechanism.
🔹3.How to Use XAU Seasonality Engine
Recommended charts: XAUUSD, intraday (5m–15m) with a HTF context (1H–4H).
1. Identify the Seasonal Regime
• Strong month → you can allow more continuation bias (still require structure).
• Neutral month → trade normally, standard criteria.
• Weak month → tighten selection, demand clean A+ conditions only.
2. Read the Month Strength Histogram
• If the score is high (e.g., 70+), the month has historically shown stronger tendency.
• If the score is low (e.g., 40 and below), expect slower conditions, deeper pullbacks, or more chop — and reduce marginal trades.
3. Use the Setup Quality Meter as the Gate
► In normal/strong months:
• A+ threshold is moderate (e.g., 70)
► In weak months:
• A+ threshold is higher (e.g., 80+)
• Optional strict mode: must also pass BOS + Sweep + FVG alignment
4. Example Trade Logic (Framework, Not Signals)
► Bullish framework in a Strong Month:
• Seasonal regime = Strong (tailwind)
• Structure supports bullish continuation (trend alignment)
• Sweep occurs into demand / liquidity grab
• Setup Quality reaches A+ threshold
• Entry: confirmation candle or retrace to key level
• SL: beyond sweep low / invalidation
• TP: nearest liquidity / prior highs / HTF level
► Weak Month rule-set (Strict Mode):
• Seasonal regime = Weak (headwind)
• Only consider trades if:
✅ BOS confirms direction
✅ Sweep occurs and rejects cleanly
✅ FVG exists recently (or is mitigated if you choose that model)
✅ Setup Quality exceeds the elevated adaptive threshold
If any one is missing → no trade
This is not meant to “predict” gold — it’s meant to enforce discipline when seasonality historically underperforms.
🔹4.Limitations and User Responsibility
► The indicator does not represent financial advice or imply performance expectations.
► Seasonality is statistical tendency, not certainty — macro conditions can override it.
► Results vary by broker feed, timeframe, and settings.
► Users should test thoroughly in simulation before applying to live markets.
► All trading decisions, risk management, and execution remain solely the responsibility of the user.
🔹5. Alerts
Optional alerts can notify when:
• a new month begins and the seasonal regime changes
• A+ criteria are met
• weak-month strict conditions pass (BOS + Sweep + FVG)
Alerts are informational only and do not constitute actionable recommendations.
Disclaimer
This script is provided for informational and educational purposes only . It does not provide financial, investment, or trading advice, and it does not guarantee profits or future performance. All decisions made based on this script are solely the responsibility of the user.
This script does not execute trades, manage risk, or replace the need for trader discretion. Market behavior can change quickly, and past behavior detected by the script does not ensure similar future outcomes.
Users should test the script on demo or simulation environments before applying it to live markets and must maintain full responsibility for their own risk management, position sizing, and trade execution.
Trading involves risk, and losses can exceed deposits. By using this script, you acknowledge that you understand and accept all associated risks.
Multi-Fractal Trading Plan [Gemini] v22Multi-Fractal Trading Plan
The Multi-Fractal Trading Plan is a quantitative market structure engine designed to filter noise and generate actionable daily strategies. Unlike standard auto-trendline indicators that clutter charts with irrelevant data, this system utilizes Fractal Geometry to categorize market liquidity into three institutional layers: Minor (Intraday), Medium (Swing), and Major (Institutional).
This tool functions as a Strategic Advisor, not just a drawing tool. It calculates the delta between price and structural pivots in real-time, alerting you when price enters high-probability "Hot Zones" and generating a live trading plan on your dashboard.
Core Features
1. Three-Tier Fractal Engine The algorithm tracks 15 distinct fractal lengths simultaneously, aggregating them into a clean hierarchy:
Minor Structure (Thin Lines): Captures high-frequency volatility for scalping.
Medium Structure (Medium Lines): Identifies significant swing points and intermediate targets.
Major Structure (Thick Lines): Maps the "Institutional" defense lines where trend reversals and major breakouts occur.
2. The Strategic Dashboard A dynamic data panel in the bottom-right eliminates analysis paralysis:
Floor & Ceiling Targets: Displays the precise price levels of the nearest Support and Resistance.
AI Logic Output: The script analyzes market conditions to generate a specific command, such as "WATCH FOR BREAKOUT", "Near Lows (Look Long?)", or "WAIT (No Setup)".
3. "Hot Zone" Detection Never miss a critical test of structure.
Dynamic Alerting: When price trades within 1% (adjustable) of a Major Trend Line, the indicator’s labels turn Bright Yellow and flash a warning (e.g., "⚠️ WATCH: MAJOR RES").
Focus: This visual cue highlights the exact moment execution is required, reducing screen fatigue.
4. The Quant Web & Markers
Pivot Validation: Deep blue fractal markers (▲/▼) identify the exact candles responsible for the structure.
Inter-Timeframe Web: Faint dotted lines connect Minor pivots directly to Major pivots, visualizing the "hidden" elasticity between short-term noise and long-term trend anchors.
5. Enterprise Stability Engine Engineered to solve the "Vertical Line" and "1970 Epoch" glitches common in Pine Script trend indicators. This engine is optimized for Futures (NQ/ES), Forex, and Crypto, ensuring stability across all timeframes (including gaps on ETH/RTH charts).
Operational Guide
Consult the Dashboard: Before executing, check the "Strategy" output. If it says "WAIT", the market is in chop. If it says "WATCH FOR BOUNCE", prepare your entry criteria.
Monitor Hot Zones: A Yellow Label indicates price is testing a major liquidity level. This is your signal to watch for a rejection wick or a high-volume breakout.
Utilize the Web: Use the faint web lines to find "confluence" where a short-term pullback aligns with a long-term trend line.
Configuration
Show History: Toggles "Ghost Lines" (Blue) to display historical structure and broken trends.
Fractal Points: Toggles the geometric pivot markers.
Hot Zone %: Adjusts the sensitivity of the Yellow Warning system (Default: 1%).
Max Line Length: A noise filter that removes stale or "spiderweb" lines that are no longer statistically relevant.
SterlCore FX Matrix [JOAT]
SterlCore FX Matrix is a multi-timeframe forex indicator that integrates market structure analysis, central bank policy proxies, currency strength correlation, session-based liquidity tracking, and volatility diagnostics into a single overlay system.
Note: This script is published as invite-only. Access requires authorization through the script's access control settings.
Why Invite-Only: The source code is protected to preserve proprietary calculation methods, composite scoring algorithms, and multi-module integration logic. The indicator combines several analytical approaches in a specific configuration that represents significant development effort. Invite-only access allows controlled distribution while maintaining the integrity of the implementation.
This Script has so much custom settings you can choose upon, to make it even more organized and tailored to your needs!
Custom settings with HeatMap and signals tailored to the daily timeframe and currency pair
## Core Functionality
This indicator addresses the challenge of synthesizing multiple analytical dimensions in forex trading. Currency markets operate across multiple timeframes simultaneously, with central bank policy shifts, cross-pair correlations, and session-specific liquidity patterns all influencing price action. Most indicators focus on a single dimension; this script attempts to integrate several.
What This Script Does:
Multi-timeframe structure analysis using synchronized EMAs across strategic (daily), tactical (4-hour), and execution (hourly) timeframes
Central bank policy pressure assessment through normalized currency index proxies
Real-time currency strength matrix tracking eight major currencies (USD, EUR, GBP, JPY, AUD, CAD, CHF, NZD)
Cross-pair correlation monitoring using configurable reference pairs
Session-based VWAP calculations with drift and range metrics for Asia, Europe, and US trading windows
Market structure detection including break-of-structure (BOS) confirmation, liquidity sweep identification, and RSI-based divergence alerts
Composite macro confluence score combining all modules with configurable weights
---
## Technical Architecture
### Multi-Timeframe Structure Lattice
The indicator calculates exponential moving averages (EMAs) across three timeframes:
Strategic EMA (default: Daily timeframe, 96-period EMA) — Anchors to longer-term monetary drift and macro flows
Tactical EMA (default: 4-hour timeframe, 55-period EMA) — Captures rotational pressure during positioning for economic data or policy events
Execution EMA (default: 1-hour timeframe, 21-period EMA) — Tracks microstructure in real time
An adaptive ATR-based channel surrounds the execution EMA to define a "value corridor" for entry consideration. Break-of-structure (BOS) logic requires price to close beyond prior swing highs/lows by a configurable ATR percentage threshold to reduce false breakouts.
### Policy Gradient & Carry Intelligence
The script uses currency index proxies (defaults: FX_IDC:EURUSD and FX_IDC:USDJPY ) to approximate central bank policy pressure. These proxies are smoothed via EMA and normalized over a lookback period.
The carryComposite calculation blends:
Normalized policy spread between base and quote currency proxies
Policy drift (difference between tactical and macro timeframe policy spreads)
Carry acceleration (rate of change in policy spread)
Carry opportunity signals appear when the composite exceeds a threshold and aligns with structure bias and currency strength dispersion.
### Currency Strength Matrix
Eight currency baskets are tracked using configurable symbol inputs (defaults use $FX_IDC pairs). Each currency's strength is normalized to a -1 to +1 scale relative to its lookback range. The heatmap table displays which currencies are dominating, allowing quick assessment of broad market moves before they appear in individual pair price action.
### Correlation Intelligence Grid
Three reference pairs (defaults: FX_IDC:EURUSD , FX_IDC:GBPUSD , FX_IDC:USDJPY ) are monitored on a higher timeframe. The script calculates correlation coefficients and assigns qualitative descriptors: "Lockstep +", "Aligned +", "Loose", "Aligned -", or "Lockstep -". A correlation consensus value feeds into the macro confluence calculation, dampening signals when reference pairs show conflicting behavior.
### Momentum, Volatility & Liquidity Stack
Dual ROC momentum — Fast and slow rate-of-change calculations prevent whipsaw from single-length oscillators
Volatility pulse — Compares current ATR to a slower baseline; signals require volatility above a floor threshold
Volatility forecast slope — Uses linear regression to project ATR 21 bars ahead, warning of imminent expansion or contraction
Liquidity pulse — Compares current volume to smoothed average; low participation is visually indicated via background tinting
### Session Awareness & Performance Console
Asia, Europe, and US trading sessions are tracked with configurable UTC windows. Each session maintains:
Live VWAP that resets at session open
Drift score quantifying price deviation from VWAP in ATR terms
Range percentage showing session expansion relative to VWAP
Session bias composite feeds into macro confluence to reduce signal aggression when all sessions are mean-reverting.
### Liquidity & Market Structure Suite
Liquidity sweeps — Detects stop hunts above prior highs or below prior lows within a configurable lookback
RSI divergence — Identifies momentum divergences using confirmed pivot points only
Supply/demand zones — Automatically generated from pivot highs/lows and projected forward for a set number of bars
### Macro Alignment Engine
The macroConfluence score combines:
Structure score (weighted average of strategic/tactical/execution EMAs)
Carry composite
Currency strength spread (base minus quote)
Momentum score
Liquidity modifier
Session bias composite
Correlation consensus
Long/short alignment signals require:
Macro confluence exceeding configurable threshold (default: 0.55)
Volatility pulse above floor threshold
Optional: Price above/below tactical EMA (execution filter toggle)
---
## Visual Elements
Candle Coloring: Candles are recolored based on macro confluence: teal for bullish alignment, magenta for bearish alignment, neutral gray for distribution phases.
Background Tint: Volatility intensity modulates chart background; bold colors indicate elevated ATR, washed-out tones suggest choppy conditions.
Labels:
Macro Align Long/Short — Primary entry signals when confluence exceeds threshold
BOS↑/↓ — Break-of-structure confirmation
Sweep↑/↓ — Liquidity sweep detection
RSI Bull/Bear Div — Momentum divergence alerts
Carry Bias± — Policy-strength alignment flags
Session Overlays: Transparent background shading indicates active trading sessions (Asia, Europe, US) with configurable opacity.
Session VWAPs: Each region's VWAP is plotted in a distinct color (teal for Asia, blue for Europe, purple for US).
## Dashboard Tables
The indicator includes several configurable information tables:
Intelligence Dashboard (top-right, default) — Displays strategic/tactical/execution bias, policy pressure, currency spread, volatility pulse, policy impulse, session drift, correlation, and macro state
Currency Heatmap (bottom-right, default) — Shows normalized strength values for all tracked currencies
Correlation Grid (bottom-left, default) — Lists reference pairs with correlation coefficients and qualitative states
Session Performance Panel (bottom-center, default) — Displays drift scores and range percentages for each session
Diagnostics Table (top-left, optional) — Additional session range metrics and liquidity pulse values
All table positions are configurable via input settings to avoid overlap with TradingView UI elements.
---
## Configuration Parameters
Multi-Timeframe Structure: All EMA timeframes and lengths are adjustable. Default strategic timeframe is Daily; tactical is 4-hour; execution is 1-hour.
Policy Proxies: Base and quote currency policy proxy symbols are user-configurable. Defaults use $FX_IDC pairs for broad compatibility.
Currency Strength: Each currency's tracking can be toggled on/off. Symbol inputs allow substitution of alternative data sources if default indices are unavailable.
Correlation References: Three reference pair symbols, timeframe, and lookback period are all configurable.
Signal Thresholds: Macro alignment trigger, volatility pulse floor, and carry opportunity threshold are adjustable to match different trading styles.
Visual Controls: Label visibility, zone display, session overlays, VWAP plotting, and all dashboard tables can be toggled independently.
---
## Technical Implementation Notes
Pine Script v6 compliant
All request.security calls use lookahead_off to prevent historical repainting
BOS, divergence, and sweep detection rely on confirmed pivot points only
Session VWAP calculations reset strictly on session boundaries
Zone objects are automatically capped and managed to respect TradingView resource limits
All calculations include division-by-zero guards and NA handling for real-time stability
---
## Usage Considerations
Timeframe Selection: The indicator is designed for forex pairs. Default timeframes (D/4H/1H) are optimized for swing and intraday trading. Scalpers may prefer shorter execution timeframes; position traders may extend strategic to weekly.
Pair Compatibility: Tested on major pairs ( FX:EURUSD , FX:GBPUSD , FX:USDJPY , OANDA:USDCHF , OANDA:AUDUSD , OANDA:USDCAD , OANDA:NZDUSD ), cross-pairs, and FX-derived CFDs. Policy proxy symbols should be adjusted to match your data feed availability.
Session Windows: Default UTC windows (Asia: 22:00-06:00, Europe: 06:00-13:00, US: 13:00-21:00) can be customized. Adjust for daylight saving time transitions as needed.
Signal Interpretation: Macro alignment signals indicate confluence across multiple dimensions but do not guarantee profitable outcomes. Use in conjunction with risk management and market context. The indicator is a tool for analysis, not a standalone trading system.
Resource Usage: With all features enabled, the script operates within TradingView's resource budgets. Disable unused modules (currency tracking, correlation grid, diagnostics) if running multiple instances on a single layout.
---
## Limitations & Compromises
Policy proxies are approximations using currency indices; actual central bank policy requires external economic analysis
Correlation calculations use price-based correlation, which may lag during regime shifts
Session VWAPs reset at session boundaries; overlapping sessions (e.g., London/NY) may show conflicting signals
Supply/demand zones are generated from pivots; false zones may appear during ranging markets
Macro confluence is a composite score; individual components may conflict, requiring discretionary interpretation
The indicator is optimized for trending and rotational markets. Performance may degrade during extended consolidation or during major economic event volatility when multiple central banks act simultaneously.
---
## Alert System
The script includes four alert conditions:
SterlCore FX Bullish Alignment — Fires when macro confluence exceeds threshold with volatility and EMA filters satisfied
SterlCore FX Bearish Alignment — Mirror of bullish logic
SterlCore FX Carry Long — Fires when carry composite, currency spread, and structure align for long bias
SterlCore FX Carry Short — Mirror of carry long logic
---
## Why This Approach
Forex markets require analysis across multiple dimensions simultaneously. A single timeframe or single indicator cannot capture the interplay between central bank policy expectations, cross-pair correlations, session-specific liquidity, and market structure. This script attempts to synthesize these elements into actionable signals while maintaining transparency about its limitations.
The composite scoring system allows traders to see when multiple factors align, reducing reliance on single-signal systems that may fail during regime changes. The modular design enables users to disable components that don't fit their trading style while retaining core functionality.
Iridescent Liquidity Prism [JOAT]Iridescent Liquidity Prism | Peer Momentum HUD
A multi-layered order-flow indicator that combines microstructure analysis, smart-money footprint detection, and intermarket momentum signals. The script uses dynamic color-shifting themes to visualize liquidity patterns, structure, and peer momentum data directly on the chart.
There is so much to choose from inside the settings, if you think it's a mess on the chart it's because you have to personally customize it based on your needs...
Core Functionality
The indicator calculates and displays several analytical layers simultaneously:
Order-Flow Imbalance (OFI): Calculates buy vs. sell volume pressure using volume-weighted price distribution within each bar. Uses an EMA filter (default: 55 periods) to smooth the signal. Values are normalized using standard deviation to identify significant imbalances.
Smart Money Footprints: Detects accumulation and distribution zones by comparing volume rate of change (ROC) against price ROC. When volume ROC exceeds a threshold (default: 65%) and price ROC is positive, accumulation is detected. When volume ROC is high but price ROC is negative, distribution is detected.
Fractal Structure Mapping: Identifies pivot highs and lows using a fractal detection algorithm (default: 5-bar period). Maintains a rolling window of recent structure points (default: 4 levels) and draws connecting lines to show trend structure.
Fair Value Gap (FVG) Detection: Automatically detects price gaps where three consecutive candles create an imbalance. Bullish FVGs occur when the current low exceeds the high two bars ago. Bearish FVGs occur when the current high is below the low two bars ago. Gaps persist for a configurable duration (default: 320 bars) and fade when price fills the gap.
Liquidity Void Detection: Identifies candles where the high-low range exceeds an ATR threshold (default: 1.7x ATR) while volume is below average (default: 65% of 20-bar average). These conditions suggest areas where liquidity may be thin.
Price/Volume Divergence: Uses linear regression to detect when price trend direction disagrees with volume trend direction. A divergence alert appears when price is trending up while volume is trending down, or vice versa.
Peer Momentum Heatmap (PMH): Calculates composite momentum scores for up to 6 symbols across 4 timeframes. Each score combines RSI (default: 14 periods) and StochRSI (default: 14 periods, 3-bar smooth) to create a momentum composite between -1 and +1. The highest absolute momentum score across all combinations is displayed in the HUD.
Custom settings using Fractal Pivots, Skeleton Structure, Pulse Liquidity Voids, Bottom Colorful HeatMaps, and Iridescent Field.
---
Visual Components
Spectrum Aura Glow: ATR-weighted bands (default: 0.25x ATR) that expand and contract around price action, indicating volatility conditions. The thickness adapts to market volatility.
Chromatic Flow Trail: A blended line combining EMA and WMA of price (default: 8-period EMA blended with WMA at 65% ratio). The trail uses gradient colors that shift based on a phase oscillator, creating an iridescent effect.
Volume Heat Projection: Creates horizontal volume profile bands at price levels (default: 14 levels). Scans recent bars (default: 150 bars) to calculate volume concentration. Each level is colored based on its volume density relative to the maximum volume level.
Structure Skeleton: Dashed lines connecting fractal pivot points. Uses two layers: a primary line (2-3px width) and an optional glow overlay (4-5px width) for enhanced visibility.
Fractal Markers: Diamond shapes placed at pivot high and low points. Color-coded: primary color for highs, secondary color for lows.
Iridescent Color Themes: Five color themes available: Iridescent (default), Pearlescent, Prismatic, ColorShift, and Metallic. Colors shift dynamically using a phase oscillator that cycles through the color spectrum based on bar index and a speed multiplier (default: 0.35).
---
HUD Console Metrics
The right-side HUD displays seven key metrics:
Flow: Shows OFI status: ▲ FLOW BUY when normalized OFI exceeds imbalance threshold (default: 2.2), ▼ FLOW SELL when below -2.2, or ◆ FLOW BAL when balanced.
Struct: Structure trend bias: ▲ STRUCT BULL when microtrend > 2, ▼ STRUCT BEAR when < -2, or ◆ STRUCT RANGE when neutral.
Smart$: Institutional activity: ◈ ACCUM when smart money index = 1, ◈ DISTRIB when = -1, or ○ IDLE when inactive.
Liquid: Liquidity state: ⚡ VOID when a liquidity void is detected, or ● NORMAL otherwise.
Diverg: Divergence status: ⚠ ALERT when price/volume divergence detected, or ✓ CLEAR when aligned.
PMH: Peer Momentum Heatmap status: Shows dominant timeframe and momentum score. Displays 🪩 for bull surge (above 0.55 threshold) or 🧨 for bear surge (below -0.55).
FVG: Fair Value Gap status: Shows active gap count or CLEAR when no gaps exist. Displays GAP LONG when bullish gap detected, GAP SHORT when bearish gap detected.
Pearlscent Color with Volume Heatmap.
Parameters and Settings
Microstructure Engine:
Analysis Depth: 20-250 bars (default: 55) - Controls OFI smoothing period
Liquidity Threshold ATR: 1.0-4.0 (default: 1.7) - Multiplier for void detection
Imbalance Ratio: 1.5-6.0 (default: 2.2) - Standard deviations for OFI significance
Smart Money Layer:
Smart Money Window: 10-150 bars (default: 24) - Period for ROC calculations
Accumulation Threshold: 40-95% (default: 65%) - Volume ROC threshold
Structural Mapping:
Fractal Pivot Period: 3-15 bars (default: 5) - Period for pivot detection
Structure Memory: 2-8 levels (default: 4) - Number of structure points to track
Volume Heat Projection:
Heat Map Lookback: 60-400 bars (default: 150) - Bars to analyze for volume profile
Heat Map Levels: 5-30 levels (default: 14) - Number of price level bands
Heat Map Opacity: 40-100% (default: 92%) - Transparency of heat map boxes
Heat Map Width Limit: 6-80 bars (default: 26) - Maximum width of heat map boxes
Heat Map Visibility Threshold: 0.0-0.5 (default: 0.08) - Minimum density to display
Iridescent Enhancements:
Visual Theme: Iridescent, Pearlescent, Prismatic, ColorShift, or Metallic
Color Shift Speed: 0.05-1.00 (default: 0.35) - Speed of color phase oscillation
Aura Thickness (ATR): 0.05-1.0 (default: 0.25) - Multiplier for aura band width
Chromatic Trail Length: 2-50 bars (default: 8) - Period for trail calculation
Trail Blend Ratio: 0.1-0.95 (default: 0.65) - EMA/WMA blend percentage
FVG Persistence: 50-600 bars (default: 320) - Bars to keep FVG boxes active
Max Active FVG Boxes: 10-200 (default: 40) - Maximum boxes on chart
FVG Base Opacity: 20-95% (default: 80%) - Transparency of FVG boxes
Peer Momentum Heatmap:
Peer Symbols: Comma-separated list of up to 6 symbols (e.g., "BTCUSD,ETHUSD")
Peer Timeframes: Comma-separated list of up to 4 timeframes (default: "60,240,D")
PMH RSI Length: 5-50 periods (default: 14)
PMH StochRSI Length: 5-50 periods (default: 14)
PMH StochRSI Smooth: 1-10 periods (default: 3)
Super Momentum Threshold: 0.2-0.95 (default: 0.55) - Threshold for surge detection
Clarity & Readability:
Liquidity Void Opacity: 5-90% (default: 30%)
Smart Money Footprint Opacity: 5-90% (default: 35%)
HUD Background Opacity: 40-95% (default: 70%)
Iridescent Field:
Field Opacity: 20-100% (default: 86%) - Background color intensity
Field Smooth Length: 10-200 bars (default: 34) - Smoothing for background gradient
---
Alerts
The indicator provides seven alert conditions:
Liquidity Void Detected - Triggers when void conditions are met
Strong Order Flow - Triggers when normalized OFI exceeds imbalance ratio
Smart Money Activity - Triggers when accumulation or distribution detected
Price/Volume Divergence - Triggers when divergence conditions occur
Structure Shift - Triggers when structure polarity changes significantly
PMH Bull Surge - Triggers when PMH exceeds positive threshold (if enabled)
PMH Bear Surge - Triggers when PMH exceeds negative threshold (if enabled)
Bull/Bear Prismatic FVG - Triggers when new FVG is detected (if FVG display enabled)
---
Usage Considerations
Performance may vary on lower timeframes due to the volume heat map calculations scanning multiple bars. Consider reducing heat map lookback or levels if experiencing slowdowns.
The PMH feature requires data requests to other symbols/timeframes, which may impact performance. Limit the number of peer symbols and timeframes for optimal performance.
FVG boxes automatically expire after the persistence period to prevent chart clutter. The maximum box limit (default: 40) prevents excessive memory usage.
Color themes affect all visual elements. Choose a theme that provides good contrast with your chart background.
The indicator is designed for overlay display. All visual elements are positioned relative to price action.
Structure lines are drawn dynamically as new pivots form. On fast-moving markets, structure may update frequently.
Volume calculations assume typical volume data availability. Symbols without volume may show incomplete data for volume-dependent features.
---
Technical Notes
Built on Pine Script v6 with dynamic request capability for PMH functionality.
Uses exponential moving averages (EMA) and weighted moving averages (WMA) for trail calculations to balance responsiveness and smoothness.
Volume profile calculation uses price level buckets. Higher levels provide finer granularity but require more computation.
Iridescent color engine uses a phase oscillator with sine wave calculations for smooth color transitions.
Box management includes automatic cleanup of expired boxes to maintain performance.
All visual elements use color gradients and transparency for smooth blending with price action.
---
Customization Examples
Intraday Scalping Setup:
Analysis Depth: 30 bars
Heat Map Lookback: 100 bars
FVG Persistence: 150 bars
PMH Window: 15 bars
Fast color shift speed: 0.5+
Macro Structure Tracking:
Analysis Depth: 100+ bars
Heat Map Lookback: 300+ bars
FVG Persistence: 500+ bars
Structure Memory: 6-8 levels
Slower color shift speed: 0.2
---
Limitations
Volume heat map calculations may be computationally intensive on lower timeframes with high lookback values.
PMH requires valid symbol names and accessible timeframes. Invalid symbols or timeframes will return no data.
FVG detection requires at least 3 bars of history. Early bars may not show FVG boxes.
Structure lines connect points but do not predict future structure. They reflect historical pivot relationships.
Color themes are aesthetic choices and do not affect calculation logic.
The indicator does not provide trading signals. All visual elements are analytical tools that require interpretation in context of market conditions.
Open Source
This indicator is open source and available for modification and distribution. The code is published with Pine Script v6 compliance. Users are free to customize parameters, modify calculations, and adapt the visual elements to their trading needs.
For questions, suggestions, or anything please talk to me in private messages or comments below!
Would love to help!
- officialjackofalltrades
Liquidity Maxing [JOAT]Liquidity Maxing - Institutional Liquidity Matrix
Introduction
Liquidity Maxing is an open-source strategy for TradingView built around institutional market structure concepts. It identifies structural shifts, evaluates trades through multi-factor confluence, and implements layered risk controls.
The strategy is designed for swing trading on 4-hour timeframes, focusing on how institutional order flow manifests in price action through structure breaks, inducements, and liquidity sweeps.
Core Functionality
Liquidity Maxing performs three primary functions:
Tracks market structure to identify when control shifts between buyers and sellers
Scores potential trades using an eight-factor confluence system
Manages position sizing and risk exposure dynamically based on volatility and user-defined limits
The goal is selective trading when multiple conditions align, rather than frequent entries.
Market Structure Engine
The structure engine tracks three key events:
Break of Structure (BOS): Price pushes beyond a prior pivot in the direction of trend
Change of Character (CHoCH): Control flips from bullish to bearish or vice versa
Inducement Sweeps (IDM): Market briefly runs stops against trend before moving in the real direction
The structure module continuously updates strong highs and lows, labeling structural shifts visually. IDM markers are optional and disabled by default to maintain chart clarity.
The trade engine requires valid structure alignment before considering entries. No structure, no trade.
Eight-Factor Confluence System
Instead of relying on a single indicator, Liquidity Maxing uses an eight-factor scoring system:
Structure alignment with current trend
RSI within healthy bands (different ranges for up and down trends)
MACD momentum agreement with direction
Volume above adaptive baseline
Price relative to main trend EMA
Session and weekend filter (configurable)
Volatility expansion/contraction via ATR shifts
Higher-timeframe EMA confirmation
Each factor contributes one point to the confluence score. The default minimum confluence threshold is 6 out of 8, but you can adjust this from 1-8 based on your preference for trade frequency versus selectivity.
Only when structure and confluence agree does the strategy proceed to risk evaluation.
Dynamic Risk Management
Risk controls are implemented in multiple layers:
ATR-based stops and targets with configurable risk-to-reward ratio (default 2:1)
Volatility-adjusted position sizing to maintain consistent risk per trade as ranges expand or compress
Daily and weekly risk budgets that halt new entries once thresholds are reached
Correlation cooldown to prevent clustered trades in the same direction
Global circuit breaker with maximum drawdown limit and emergency kill switch
If any guardrail is breached, the strategy will not open new positions. The dashboard clearly displays risk state for transparency.
Market Presets
The strategy includes configuration presets optimized for different market types:
Crypto (BTC/ETH): RSI bands 70/30, volume multiplier 1.2, enhanced ATR scaling
Forex Majors: RSI bands 75/25, volume multiplier 1.5
Indices (SPY/QQQ): RSI bands 70/30, volume multiplier 1.3
Custom: Default values for user customization
For crypto assets, the strategy automatically applies ATR volatility scaling to account for higher volatility characteristics.
Monitoring and Dashboards
The strategy includes optional monitoring layers:
Risk Operations Dashboard (top-right):
Trend state
Confluence score
ATR value
Current position size percentage
Global drawdown
Daily and weekly risk consumption
Correlation guard state
Alert mode status
Performance Console (top-left):
Net profit
Current equity
Win rate percentage
Average trade value
Sharpe-style ratio (rolling 50-bar window)
Profit factor
Open trade count
Optional risk tint on chart background provides visual indication of "safe to trade" versus "halted" state.
All visualization elements can be toggled on/off from the inputs for clean chart viewing or full telemetry during parameter tuning.
Alerts and Automation
The strategy supports alert integration with two formats:
Standard alerts: Human-readable messages for long, short, and risk-halt conditions
Webhook format: JSON-formatted payloads ready for external execution systems (optional)
Alert messages are predictable and unambiguous, suitable for manual review or automated forwarding to execution engines.
Built-in Validation Suite
The strategy includes an optional validation layer that can be enabled from inputs. It checks:
Internal consistency of structure and confluence metrics
Sanity and ordering of risk parameters
Position sizing compliance with user-defined floors and caps
This validation is optional and not required for trading, but provides transparency into system operation during development or troubleshooting.
Strategy Parameters
Market Presets:
Configuration Preset: Choose between Crypto (BTC/ETH), Forex Majors, Indices (SPY/QQQ), or Custom
Market Structure Architecture:
Pivot Length: Default 5 bars
Filter by Inducement (IDM): Default enabled
Visualize Structure: Default enabled
Structure Lookback: Default 50 bars
Risk & Capital Preservation:
Risk:Reward Ratio: Default 2.0
ATR Period: Default 14
ATR Multiplier (Stop): Default 2.0
Max Drawdown Circuit Breaker: Default 10%
Risk per Trade (% Equity): Default 1.5%
Daily Risk Limit: Default 6%
Weekly Risk Limit: Default 12%
Min Position Size (% Equity): Default 0.25%
Max Position Size (% Equity): Default 5%
Correlation Cooldown (bars): Default 3
Emergency Kill Switch: Default disabled
Signal Confluence:
RSI Length: Default 14
Trend EMA: Default 200
HTF Confirmation TF: Default Daily
Allow Weekend Trading: Default enabled
Minimum Confluence Score (0-8): Default 6
Backtesting Considerations
When backtesting this strategy, consider the following:
Commission: Default 0.05% (adjustable in strategy settings)
Initial Capital: Default $100,000 (adjustable)
Position Sizing: Uses percentage of equity (default 2% per trade)
Timeframe: Optimized for 4-hour charts, though can be tested on other timeframes
Results will vary significantly based on:
Market conditions and volatility regimes
Parameter settings, especially confluence threshold
Risk limit configuration
Symbol characteristics (crypto vs forex vs equities)
Past performance does not guarantee future results. Win rate, profit factor, and other metrics should be evaluated in context of drawdown periods, trade frequency, and market conditions.
How to Use This Strategy
This is a framework that requires understanding and parameter tuning, not a one-size-fits-all solution.
Recommended workflow:
Start on 4-hour timeframe with default parameters and appropriate market preset
Run backtests and study performance console metrics: focus on drawdown behavior, win rate, profit factor, and trade frequency
Adjust confluence threshold to match your risk appetite—higher thresholds mean fewer but more selective trades
Set realistic daily and weekly risk budgets appropriate for your account size and risk tolerance
Consider ATR multiplier adjustments based on market volatility characteristics
Only connect alerts or automation after thorough testing and parameter validation
Treat this as a risk framework with an integrated entry engine, not merely an entry signal generator. The risk controls are as important as the trade signals.
Strategy Limitations
Designed for swing trading timeframes; may not perform optimally on very short timeframes
Requires sufficient market structure to identify pivots; may struggle in choppy or low-volatility environments
Crypto markets require different parameter tuning than traditional markets
Risk limits may prevent entries during favorable setups if daily/weekly budgets are exhausted
Correlation cooldown may delay entries that would otherwise be valid
Backtesting results depend on data quality and may not reflect live trading with slippage
Design Philosophy
Many indicators tell you when price crossed a moving average or RSI left oversold. This strategy addresses questions institutional traders ask:
Who is in control of the market right now?
Is this move structurally significant or just noise?
Do I want to add more risk given what I've already done today/week?
If I'm wrong, exactly how painful can this be?
The strategy provides disciplined, repeatable answers to these questions through systematic structure analysis, confluence filtering, and multi-layer risk management.
Technical Implementation
The strategy uses Pine Script v6 with:
Custom types for structure, confluence, and risk state management
Functional programming approach for reusable calculations
State management through persistent variables
Optional visual elements that can be toggled independently
The code is open-source and can be modified to suit individual needs. All important logic is visible in the source code.
Disclaimer
This script is provided for educational and informational purposes only. It is not intended as financial, investment, trading, or any other type of advice or recommendation. Trading involves substantial risk of loss and is not suitable for all investors. Past performance, whether real or indicated by historical tests of strategies, is not indicative of future results.
No representation is being made that any account will or is likely to achieve profits or losses similar to those shown. In fact, there are frequently sharp differences between backtested results and actual results subsequently achieved by any particular trading strategy.
The user should be aware of the risks involved in trading and should trade only with risk capital. The authors and publishers of this script are not responsible for any losses or damages, including without limitation, any loss of profit, which may arise directly or indirectly from use of or reliance on this script.
This strategy uses technical analysis methods and indicators that are not guaranteed to be accurate or profitable. Market conditions change, and strategies that worked in the past may not work in the future. Users should thoroughly test any strategy in a paper trading environment before risking real capital.
Commission and slippage settings in backtests may not accurately reflect live trading conditions. Real trading results will vary based on execution quality, market liquidity, and other factors not captured in backtesting.
The user assumes full responsibility for all trading decisions made using this script. Always consult with a qualified financial advisor before making investment decisions.
Enjoy - officialjackofalltrades
V3 Valentini Pro Scalper [Dashboard]Gemini 3.0 pro's take on Fabio Valentini's world #1 strategy scalp 12/19/2025
Adaptive Z-Score Oscillator [QuantAlgo]🟢 Overview
The Adaptive Z-Score Oscillator transforms price action into statistical significance measurements by calculating how many standard deviations the current price deviates from its moving average baseline, then dynamically adjusting threshold levels based on historical distribution patterns. Unlike traditional oscillators that rely on fixed overbought/oversold levels, this indicator employs percentile-based adaptive thresholds that automatically calibrate to changing market volatility regimes and statistical characteristics. By offering both adaptive and fixed threshold modes alongside multiple moving average types and customizable smoothing, the indicator provides traders and investors with a robust framework for identifying extreme price deviations, mean reversion opportunities, and underlying trend conditions through the visualization of price behavior within a statistical distribution context.
🟢 How It Works
The indicator begins by establishing a dynamic baseline using a user-selected moving average type applied to closing prices over the specified length period, then calculates the standard deviation to measure price dispersion:
basis = ma(close, length, maType)
stdev = ta.stdev(close, length)
The core Z-Score calculation quantifies how many standard deviations the current price sits above or below the moving average basis, creating a normalized oscillator that facilitates cross-asset and cross-timeframe comparisons:
zScore = stdev != 0 ? (close - basis) / stdev : 0
smoothedZ = ma(zScore, smooth, maType)
The adaptive threshold mechanism employs percentile calculations over a historical lookback period to determine statistically significant extreme zones. Rather than using fixed levels like ±2.0, the indicator identifies where a specified percentage of historical Z-Score readings have fallen, automatically adjusting to market regime changes:
upperThreshold = adaptive ? ta.percentile_linear_interpolation(smoothedZ, percentilePeriod, upperPercentile) : fixedUpper
lowerThreshold = adaptive ? ta.percentile_linear_interpolation(smoothedZ, percentilePeriod, lowerPercentile) : fixedLower
The visualization architecture creates a four-tier coloring system that distinguishes between extreme conditions (beyond the adaptive thresholds) and moderate conditions (between the midpoint and threshold levels), providing visual gradation of statistical significance through opacity variations and immediate recognition of distribution extremes.
🟢 How to Use This Indicator
▶ Overbought and Oversold Identification:
The indicator identifies potential overbought conditions when the smoothed Z-Score crosses above the upper threshold, indicating that price has deviated to a statistically extreme level above its mean. Conversely, oversold conditions emerge when the Z-Score crosses below the lower threshold, signaling statistically significant downward deviation. In adaptive mode (default), these thresholds automatically adjust to the asset's historical behavior, i.e., during high volatility periods, the thresholds expand to accommodate wider price swings, while during low volatility regimes, they contract to capture smaller deviations as significant. This dynamic calibration reduce false signals that plague fixed-level oscillators when market character shifts between volatile and ranging conditions.
▶ Mean Reversion Trading Applications:
The Z-Score framework excels at identifying mean reversion opportunities by highlighting when price has stretched too far from its statistical equilibrium. When the oscillator reaches extreme bearish levels (below the lower threshold with deep red coloring), it suggests price has become statistically oversold and may snap back toward the mean, presenting potential long entry opportunities for mean reversion traders. Symmetrically, extreme bullish readings (above the upper threshold with bright green coloring) indicate potential short opportunities or long exit points as price becomes statistically overbought. The moderate zones (lighter colors between midpoint and threshold) serve as early warning areas where traders can prepare for potential reversals, while exits from extreme zones (crossing back inside the thresholds) often provide confirmation that mean reversion is underway.
▶ Trend and Distribution Analysis:
Beyond discrete overbought/oversold signals, the histogram's color pattern and shape reveal the underlying trend structure and distribution characteristics. Sustained periods where the Z-Score oscillates primarily in positive territory (green bars) indicate a bullish trend where price consistently trades above its moving average baseline, even if not reaching extreme levels. Conversely, predominant negative readings (red bars) suggest bearish trend conditions. The distribution shape itself provides insight into market behavior, e.g., a narrow, centered distribution clustering near zero indicates tight ranging conditions with price respecting the mean, while a wide distribution with frequent extreme readings reveals volatile trending or choppy conditions. Asymmetric distributions skewed heavily toward one side demonstrate persistent directional bias, whereas balanced distributions suggest equilibrium between bulls and bears.
▶ Built-in Alerts:
Seven alert conditions enable automated monitoring of statistical extremes and trend transitions. Enter Overbought and Enter Oversold alerts trigger when the Z-Score crosses into extreme zones, providing early warnings of potential reversal setups. Exit Overbought and Exit Oversold alerts signal when price begins reverting from extremes, offering confirmation that mean reversion has initiated. Zero Cross Up and Zero Cross Down alerts identify transitions through the neutral line, indicating shifts between above-mean and below-mean price action that can signal trend changes. The Extreme Zone Entry alert fires on any extreme threshold penetration regardless of direction, allowing unified monitoring of both overbought and oversold opportunities.
▶ Color Customization:
Six visual themes (Classic, Aqua, Cosmic, Ember, Neon, plus Custom) accommodate different chart backgrounds and aesthetic preferences, ensuring optimal contrast and readability across trading platforms. The bar transparency control (0-90%) allows fine-tuning of visual prominence, with minimal transparency creating bold, attention-grabbing bars for primary analysis, while higher transparency values produce subtle background context when using the oscillator alongside other indicators. The extreme and moderate zone coloring system uses automatic opacity variation to create instant visual hierarchy, with darkest colors highlight the most statistically significant deviations demanding immediate attention, while lighter shades mark developing conditions that warrant monitoring but may not yet justify action. Optional candle coloring extends the Z-Score color scheme directly to the price candles on the main chart, enabling traders to instantly recognize statistical extremes and trend conditions without needing to reference the oscillator panel, creating a unified visual experience where both price action and statistical analysis share the same color language.
Liquidity Entry Triggers (4-Model System) | WarRoomXYZLiquidity Entry Triggers is an open-source, price-action-based analytical framework designed to highlight recurring institutional liquidity behaviors that appear across all liquid markets.
The script focuses on how and where liquidity is taken, rather than attempting to predict direction using oscillators or lagging indicators.
It is optimized for XAUUSD, FX pairs, indices, and crypto , particularly on 1m–15m timeframes where session behavior and liquidity reactions are most visible.
This tool is not a buy/sell signal generator .
It provides contextual entry zones based on structural liquidity logic, allowing traders to apply their own execution rules.
Core Philosophy
Markets move because of:
•Trapped traders
•Forced liquidations
•Session-based liquidity cycles
•Reactions at prior institutional participation zones
This script visualizes four repeatable entry triggers that emerge from those mechanisms.
🔹 1. Failed Breakout / Trapped Trader Model
When price breaks a clearly defined range high or low, breakout traders often enter expecting continuation.
If price fails to hold outside the range and closes back inside, those traders become trapped.
The script detects:
•Breaks beyond recent highs/lows
•Immediate rejection back into the range
•Structural failure of momentum
These conditions frequently lead to mean reversion or reversal moves as trapped traders exit and fuel movement in the opposite direction.
Markers are plotted at the point of failure to highlight potential trap zones.
🔹 2. Liquidation Flush Detection
Sharp impulsive candles with abnormally large wicks often represent liquidation cascades rather than healthy trend continuation.
The script identifies liquidation behavior by measuring:
•Wick-to-body imbalance
•Sudden expansion followed by rejection
•Temporary price inefficiencies
These flushes commonly occur near:
•Session highs/lows
•Range extremes
•Trend exhaustion points
Such events often lead to rebalance moves , where price partially or fully fills the wick.
🔹 3. Orderblock Reaction Zones
Orderblocks represent areas where heavy participation occurred before a strong displacement move.
The script highlights:
•Clean bullish and bearish orderblock structures
•Zones formed during consolidation prior to expansion
•Areas likely to be defended when revisited
Orderblocks with minimal noise and clean departure are prioritized, as they often reflect institutional positioning rather than retail activity.
These zones are intended as reaction areas , not automatic entry signals.
🔹 4. London Session Liquidity Sweep Model
The London session frequently establishes the initial daily high or low.
Later in the session or during New York, price often:
•Sweeps internal liquidity around that level
•Rejects after the sweep
•Continues with the higher-timeframe bias
The script monitors London session behavior and marks:
•Liquidity runs above/below London highs and lows
•Rejections back inside the prior structure
This model is especially effective when combined with broader daily context.
🔹4. How the Components Work Together
The framework is designed as a context stack , not a checklist of signals:
Liquidity Event → Location → Timing → Trader Execution
Each model reinforces the others:
•Failed breakouts often occur after liquidity sweeps
•Liquidation wicks frequently form near orderblocks
•London sweeps often trigger failed momentum moves
•Confluence increases probability, not certainty
🔹 Practical Usage Guide
✔ Identify context
Determine whether price is approaching a range extreme, session level, or prior participation zone.
✔ Wait for a liquidity event
Look for a sweep, failed breakout, or liquidation wick.
✔ Observe reaction
Rejection, displacement, or reclaim behavior provides confirmation.
✔ Execute manually
Stops are commonly placed beyond the liquidity extreme.
Targets are typically internal liquidity, prior highs/lows, or imbalance zones.
The indicator does not manage trades or enforce rules.
Execution and risk management remain the trader’s responsibility.
🔹 5. Originality & Design Notes
This script does not replicate or bundle existing indicators.
It introduces:
•A multi-model liquidity entry framework
•Structural failed breakout detection
•Wick-based liquidation imbalance logic
•Session-aware liquidity sweep visualization
•A unified, minimal, non-lagging design
All concepts are based on observable market behavior and integrated into a single analytical tool.
🔹 6. Suitable Markets & Timeframes
Works best on:
•XAUUSD
•Major FX pairs
•Indices
•Liquid crypto markets
Recommended timeframes:
•1m
•5m
•15m
•30m
🔹7. Limitations & Notes
•This is an analytical framework , not a trading system
•All markings are confirmed at candle close (non-repainting)
•No open interest or order flow data is used
•Results depend on user interpretation and execution
•Best used alongside session bias and higher-timeframe structure
Disclaimer
This script is provided for educational and informational purposes only.
It does not constitute financial advice, investment advice, or a recommendation to buy or sell any instrument.
Trading involves risk, and losses can exceed initial deposits.
The author assumes no responsibility for trading decisions made using this tool.
Users are strongly encouraged to test this script in demo or simulation environments and to apply proper risk management, position sizing, and personal discretion at all times.
By using this script, you acknowledge and accept all associated risks.
Bollinger Bands Forecast [QuantAlgo]🟢 Overview
Bollinger Bands are widely recognized for mapping volatility boundaries around price action, but they inherently lag behind market movement since they calculate based on completed bars. The Bollinger Bands Forecast addresses this limitation by adding a predictive layer that attempts to project where the upper band, lower band, and basis line might position in the future. The indicator provides three unique analytical models for generating these projections: one examines swing structure and breakout patterns, another integrates volume flow and accumulation metrics, while the third applies statistical trend fitting. Traders can select whichever methodology aligns with their market view or trading style to gain visibility into potential future volatility zones that could inform position planning, risk management, and timing decisions across various asset classes and timeframes.
🟢 How It Works
The core calculation begins with traditional Bollinger Bands: a moving average basis line (configurable as SMA, EMA, SMMA/RMA, WMA, or VWMA) with upper and lower bands positioned at a specified number of standard deviations away. The forecasting extension works by first generating predicted price values for upcoming bars using the selected method. These projected prices then feed into a rolling calculation that simulates how the basis line would update bar by bar, respecting the mathematical properties of the chosen moving average type. As each new forecasted price enters the calculation window, the oldest historical price drops out, mimicking the natural progression of the moving average. The system recalculates standard deviation across this evolving price window and applies the multiplier to determine where upper and lower bands would theoretically sit. This process repeats for each of the forecasted bars, creating a connected chain of potential future band positions that render as dashed lines on the chart.
🟢 Key Features
1. Market Structure Model
This forecasting approach interprets price through the lens of swing analysis and structural patterns. The algorithm identifies pivot highs and lows across a definable lookback window, then tracks whether price is forming higher highs and higher lows (bullish structure) or lower highs and lower lows (bearish structure). The system looks for break of structure (BOS) when price pushes beyond a previous swing point in the trending direction, or change of character (CHoCH) when price starts creating opposing swing patterns.
When projecting future prices, the model considers current distance from recent swing levels and the strength of the established trend (measured by counting higher highs versus lower lows). If bullish structure dominates and price sits near a swing low, the forecast biases upward. Conversely, bearish structure near a swing high produces downward bias. ATR scaling ensures the projection magnitude relates to actual market volatility.
Practical Implications for Traders:
Useful when you trade based on swing points and structural breaks
The Structure Influence slider (0 to 1) lets you dial in how much weight structure analysis carries versus pure trend
Helps visualize where bands could form around key structural levels you're watching
Works better in trending conditions where structure patterns are clearer
Might be less effective in choppy, sideways markets without defined swings
2. Volume-Weighted Model
This method attempts to incorporate volume flow into the price forecast. It combines three volume-based metrics: On-Balance Volume (OBV) to track cumulative buying/selling pressure, the Accumulation/Distribution Line to measure money flow, and volume-weighted price changes to emphasize moves that occur on high volume. The algorithm calculates the slope of these indicators to determine if volume is confirming price direction or diverging from it.
Volume spikes above a configurable threshold are flagged as potentially significant, with the direction of the spike (whether it occurred on an up bar or down bar) influencing the forecast. When OBV, A/D Line, and volume momentum all align in the same direction, the model projects stronger moves. When they conflict or show weak volume support, the forecast becomes more conservative.
Practical Implications for Traders:
Relevant if you use volume analysis to confirm price moves
More meaningful in markets with reliable volume data
The Volume Influence parameter (0 to 1) controls how much volume factors into the projection
Volume Spike Threshold adjusts sensitivity to what constitutes unusual volume
Helps spot scenarios where volume doesn't support a move, suggesting possible consolidation
Might be less effective in low-liquidity instruments or markets where volume reporting is unreliable
3. Linear Regression Model
The simplest of the three methods, linear regression fits a straight line through recent price data using least-squares mathematics and extends that line forward. This creates a clean trend projection without conditional logic or interpretation of market characteristics. The forecast simply asks: if the recent trend continues at its current rate of change, where would price be in 10 or 20 bars?
Practical Implications for traders:
Provides a neutral, mathematical baseline for comparison
Works well when trends are steady and consistent
Can be useful for backtesting since results are deterministic
Requires minimal configuration beyond lookback period
Might not adapt to changing market conditions as dynamically as the other methods
Best suited for trending markets rather than ranging or volatile conditions
🟢 Universal Applications Across All Models
Regardless of which forecasting method you select, the indicator projects future Bollinger Band positions that may help with:
▶ Pre-planning entries and exits: See where potential support (lower band) or resistance (upper band) might develop before price gets there
▶ Volatility context: Observe whether forecasted bands are widening (suggesting potential volatility expansion) or narrowing (possible compression or squeeze setup)
▶ Target setting: Reference projected band levels when determining profit targets or stop placement
▶ Mean reversion scenarios: Visualize potential paths back toward the basis line when price extends to a band extreme
▶ Breakout anticipation: Consider where upper or lower bands might sit if price begins a strong directional move
▶ Strategy development: Build trading rules around forecasted band interactions, such as entering when price is projected to return to the basis or exit when forecasts show band expansion
▶ Method comparison: Switch between the three forecasting models to see if they agree or diverge, potentially using consensus as a confidence filter
It's critical to understand that these forecasts are projections based on recent market behavior. Markets are complex systems influenced by countless factors that cannot be captured in a technical calculation or predicted perfectly. The forecasted bands represent one possible scenario of how volatility might unfold, so actual price action may still diverge from these projections. Past performance and historical patterns provide no assurance of future results. Use these forecasts as one input within a broader trading framework that includes proper risk management, position sizing, and multiple forms of analysis. The value lies not in prediction accuracy but in helping you think probabilistically about potential market states and plan accordingly.
Stochastic RSI Forecast [QuantAlgo]🟢 Overview
The Stochastic RSI Forecast extends the classic momentum oscillator by projecting potential future K and D line values up to 10 bars ahead. Unlike traditional indicators that only reflect historical price action, this indicator uses three proprietary forecasting models, each operating on different market data inputs (price structure, volume metrics, or linear trend), to explore potential price paths. This unique approach allows traders to form probabilistic expectations about future momentum states and incorporate these projections into both discretionary and algorithmic trading and/or analysis.
🟢 How It Works
The indicator operates through a multi-stage calculation process that extends the RSI-to-Stochastic chain forward in time. First, it generates potential future price values using one of three selectable forecasting methods, each analyzing different market dimensions (structure, volume, or trend). These projected prices are then processed through an iterative RSI calculation that maintains continuity with historical gain/loss averages, producing forecasted RSI values. Finally, the system applies the full stochastic transformation (calculating the position of each forecasted RSI within its range, smoothing with K and D periods) to project potential future oscillator values.
The forecasting models adapt to market conditions by analyzing configurable lookback periods and recalculating projections on every bar update. The implementation preserves the mathematical properties of the underlying RSI calculation while extrapolating momentum trajectories, creating visual continuity between historical and forecasted values displayed as semi-transparent dashed lines extending beyond the current bar.
🟢 Key Features
1. Market Structure Model
This algorithm applies price action analysis by tracking break of structure (BOS) and change of character (CHoCH) patterns to identify potential order flow direction. The system detects swing highs and lows using configurable pivot lengths, then analyzes sequences of higher highs or lower lows to determine bullish or bearish structure bias. When price approaches recent swing points, the forecast projects moves in alignment with the established structure, scaled by ATR (Average True Range) for volatility adjustment.
Potential Benefits for Traders:
Explores potential momentum continuation scenarios during established trends
Identifies areas where structure changes might influence momentum
Could be useful for swing traders and position traders who incorporate structure-based analysis
The Structure Influence parameter (0-1 scale) allows blending between pure trend following and structure-weighted forecasts
Helps visualize potential trend exhaustion through weakening structure patterns
2. Volume-Weighted Model
This model analyzes volume patterns by combining On-Balance Volume (OBV), Accumulation/Distribution Line, and volume-weighted price returns to assess potential capital flow. The algorithm calculates directional volume momentum and identifies volume spikes above customizable thresholds to determine accumulation or distribution phases. When volume indicators align directionally, the forecast projects stronger potential moves; when volume diverges from price trends, it suggests possible reversals or consolidation.
Potential Benefits for Traders:
Incorporates volume analysis into momentum forecasting
Attempts to filter price action by volume support or lack thereof
Could be more relevant in markets where volume data is reliable (equities, crypto, major forex pairs)
Volume Influence parameter (0-1 scale) enables adaptation to different market liquidity profiles
Highlights volume climax patterns that sometimes precede trend changes
Could be valuable for traders who incorporate volume confirmation in their analysis
3. Linear Regression Model
This mathematical approach applies least-squares regression fitting to project price trends based on recent price data. Unlike the conditional logic of the other methods, linear regression provides straightforward trend extrapolation based on the best-fit line through the lookback period.
Potential Benefits for Traders:
Delivers consistent, reproducible forecasts based on statistical principles
Works better in trending markets with clear directional bias
Useful for systematic traders building quantitative strategies requiring stable inputs
Minimal parameter sensitivity (primarily controlled by lookback period)
Computationally efficient with fast recalculation on every bar
Serves as a baseline to compare against the more complex structure and volume methods
🟢 Universal Applications Across All Models
Each forecasting method projects potential future stochastic RSI values (K and D lines), which traders can use to:
▶ Anticipate potential crossovers: Visualize possible K/D crosses several bars ahead
▶ Explore overbought/oversold scenarios: Forecast when momentum might return from extreme zones
▶ Assess divergences: Evaluate how oscillator divergences might develop
▶ Inform entry timing: Consider potential points along the forecasted momentum curve for trade entry
▶ Develop systematic strategies: Build rules based on forecasted crossovers, slope changes, or threshold levels
▶ Adapt to market conditions: Switch between methods based on current market character (trending vs range-bound, high vs low volume)
In short, the indicator's flexibility allows traders to combine forecasting projections with traditional stochastic signals, using historical K/D for immediate reference while considering forecasted values for planning and analysis. As with all technical analysis tools, the forecasts represent one possible scenario among many and should be used as part of a broader trading methodology rather than as standalone signals.
Session Sweep System – WarRoomXYZ V1WarRoom Session Sweep System v1 is a open-source institutional trading framework built to identify liquidity behavior across Asia, London, and New York sessions.
It combines session-based liquidity mapping, sweep detection, daily expansion modeling, and trend confirmation into a unified, timing-driven system optimized for XAUUSD, FX pairs, indices, and any instrument with session-dependent volatility.
This tool does not attempt to predict direction with arbitrary oscillators.
Instead, it focuses on the underlying market mechanisms that drive price:
liquidity, timing, expansion, and trend alignment.
Below is a detailed explanation of what the script does, how its components work, and how traders can use it effectively.
🔹 1. Session Liquidity Mapping
The script automatically identifies the Asia (00:00–06:00 GMT), London (07:00–12:00 GMT), and New York (13:00–17:00 GMT) sessions and builds real-time session ranges.
Each session creates a liquidity pool.
Trading institutions frequently sweep the high or low of one session before delivering the real move in the next session.
This script captures that behavior by:
►Drawing session range boxes
►Tracking previous session highs/lows
►Highlighting high-probability sweep locations
These ranges are essential reference points for timing entries and exits.
🔹 2. Liquidity Sweep Detection (Buy & Sell Sweeps)
The indicator identifies when price runs a previous session high/low and rejects back inside the range, which is commonly interpreted as a liquidity sweep.
The following sweep types are monitored:
►London sweeping Asia
►New York sweeping London
►Asia sweeping New York
►Daily sweep of PDH/PDL
Sweeps signal that liquidity has been collected and that a potential reversal or continuation is likely.
These are marked clearly on the chart for real-time decision-making.
🔹 3. Killzone Timing Model (GMT Time)
Market manipulation and expansion often occur during specific time windows.
The script highlights these institutional killzones:
►London Killzone: 07:00–10:00 GMT
►New York Killzone: 13:30–15:30 GMT
►NY PM Session: 19:00–21:00 GMT
Sweeps occurring inside these windows carry a significantly higher probability.
The timing layer helps filter out low-quality setups.
🔹 4. Daily Range & ADR Expansion Engine
A dedicated panel displays:
►Current day range
►ADR (Average Daily Range)
►Expansion stage (Early / Developed / Extended)
►PDH/PDL swept or intact
►Overall session bias
This allows traders to understand whether the daily move is likely to continue or reverse.
For example:
►Early expansion → trend continuation likely
►Extended expansion → reversal setups become more probable
This is useful for intraday targets and risk management.
🔹 5. MA Cloud Trend Model (Fast/Slow Structure)
To align liquidity behavior with directional conviction, the script includes a configurable MA engine:
►Fast & slow MA
►MA cloud
►Slope-based trend coloring
►Trend background
►MA cross alerts
The cloud provides trend confirmation without relying on oscillators.
Trades are higher quality when the sweep direction aligns with the MA trend.
🔹 6. How the Components Work Together
The script integrates several institutional concepts into one coherent model:
►Sessions define liquidity pools
►Sweeps identify stop-hunts and reversals
►Killzones define optimal timing
►MA Cloud confirms directional bias
►ADR engine indicates expansion potential
This creates a structured framework:
Sweep → Timing → Trend → Expansion → Execution
Each component strengthens the others, forming a robust decision-making model.
🔹 7. How to Use the Indicator (Practical Guide)
✔ Look for a sweep of a previous session level
When price runs a session high/low and closes back inside, liquidity has likely been collected.
✔ Confirm timing
Sweeps inside London or NY killzones tend to produce the strongest moves.
✔ Confirm trend
Use MA cloud direction and slope:
►Cloud green → long setups preferred
►Cloud red → short setups preferred
✔ Check ADR panel
If the day has already expanded significantly, reversal setups are more likely.
If expansion is still early, continuation setups are favored.
✔ Plan your trade
Common targets include:
►Opposite side of session range
►ADR High/Low
►PDH/PDL
Stops are typically placed beyond the sweep wick.
This creates a repeatable, rule-based approach to intraday liquidity trading.
🔹 8. Why This Script Is Original
This is not a mashup of existing open-source indicators.
It introduces:
►A custom session-linked liquidity sweep engine
►A structured daily expansion model
►Integrated killzone timing aligned with GMT
►A unified bias panel merging sweeps, ADR, and session manipulation
►A trend confirmation layer designed around session behavior
While it uses known institutional concepts, their integration, execution, and timing framework are unique, purpose-built, and not directly found in open-source scripts.
🔹 9. Suitable Markets
This indicator works best on:
►XAUUSD
►Major FX pairs
►US indices
►Synthetic markets with session cycles
Ideal timeframes: 1m, 5m, 15m, 30m
🔹 10. Limitations / Notes
This is an analytical tool, not a buy/sell signal generator
All sweeps are confirmed at candle close (non-repaint)
The tool assumes GMT session windows unless chart time differs
Users must practice risk management and entry triggers manually
Disclaimer
This script is provided for informational and educational purposes only. It does not provide financial, investment, or trading advice, and it does not guarantee profits or future performance. All decisions made based on this script are solely the responsibility of the user.
This script does not execute trades, manage risk, or replace the need for trader discretion. Market behavior can change quickly, and past behavior detected by the script does not ensure similar future outcomes.
Users should test the script on demo or simulation environments before applying it to live markets and must maintain full responsibility for their own risk management, position sizing, and trade execution.
Trading involves risk, and losses can exceed deposits. By using this script, you acknowledge that you understand and accept all associated risks.
Profitable Pair Correlation Divergence Scanner v6This strategy identifies divergence opportunities between two correlated assets using a combination of Z-Score spread analysis, trend confirmation, RSI & MACD momentum checks, correlation filters, and ATR-based stop-loss/take-profit management. It’s optimized for positive P&L and realistic trade execution.
Key Features:
Pair Divergence Detection:
Measures deviation between returns of two assets and identifies overbought/oversold spread conditions using Z-Score.
Trend Alignment:
Trades only in the direction of the primary asset’s trend using a fast EMA vs slow EMA filter.
Momentum Confirmation:
Confirms trades with RSI and MACD to reduce false signals.
Correlation Filter:
Ensures the pair is strongly correlated before taking trades, avoiding noisy signals.
Risk Management:
Dynamic ATR-based stop-loss and take-profit ensures proper reward-to-risk ratio.
Exit Conditions:
Automatically closes positions when Z-Score normalizes, or ATR-based exits are hit.
How It Works:
Calculate Returns:
Computes returns for both assets over the selected timeframe.
Z-Score Spread:
Calculates the spread between returns and normalizes it using moving average and standard deviation.
Trend Filter:
Only takes long trades if the fast EMA is above the slow EMA, and short trades if the fast EMA is below the slow EMA.
Momentum Confirmation:
Confirms trade direction with RSI (>50 for longs, <50 for shorts) and MACD alignment.
Correlation Check:
Ensures the pair’s recent correlation is strong enough to validate divergence signals.
Trade Execution:
Opens positions when Z-Score crosses thresholds and all conditions align. Positions close when Z-Score normalizes or ATR-based SL/TP is hit.
Plot Explanation:
Z-Score: Blue line shows divergence magnitude.
Entry Levels: Red/Green lines mark long/short thresholds.
Exit Zone: Gray lines show normalization zone.
EMA Trend Lines: Purple (fast), Orange (slow) for trend alignment.
Correlation: Teal overlay shows current correlation strength.
Usage Tips:
Use highly correlated pairs for best results (e.g., EURUSD/GBPUSD).
Run on higher timeframe charts (1h or 4h) to reduce noise.
Adjust ATR multiplier based on volatility to avoid premature stops.
Combine with alerts for automated notifications or webhook execution.
Conclusion:
The Profitable Pair Correlation Divergence Scanner v6 is designed for traders who want systematic, low-risk, positive P&L trading opportunities with minimal manual monitoring. By combining trend alignment, momentum confirmation, correlation filters, and dynamic exits, it reduces false signals and improves execution reliability.
Run it on TradingView and watch how it captures divergence opportunities while maintaining positive P&L across trades.
Momentum Factor Model [QuantAlgo]🟢 Overview
The Momentum Factor Model is a multi-horizon momentum analysis system that combines weighted return calculations with risk-adjusted price projections to identify and track persistent directional trends. The indicator employs a quantitative approach by measuring momentum across multiple timeframes simultaneously, applying exponential decay weighting to balance recent versus historical price action, and constructing volatility-normalized boundaries for trend validation. This factor-based methodology provides traders and investors with a systematic framework for momentum regime identification, trend persistence evaluation, and dynamic support/resistance determination across diverse market conditions and timeframes.
🟢 How It Works
The indicator constructs a composite momentum factor by calculating percentage returns over three distinct lookback periods (1, 3, and 5 bars) and combining them using exponentially decayed weights. The momentum decay parameter controls the relative importance of each timeframe, with higher decay values creating more balanced weighting between recent and historical momentum, while lower values emphasize immediate price action. This weighted momentum factor captures the multi-dimensional nature of trend strength rather than relying on a single timeframe measurement.
The expected return is derived by smoothing the momentum factor over a user-defined period, establishing a baseline for anticipated price movement based on recent momentum characteristics. This expected return then projects a factor-based price estimate, which undergoes risk adjustment through volatility normalization, creating a price estimate that accounts for both directional bias and market volatility conditions.
🟢 How to Use It
▶ Enter Long positions when the momentum factor dots (⏺) transition from red to green (bullish) , indicating the momentum factor model has confirmed positive directional bias. The color change represents a validated shift where the factor line has broken through the lower boundary and begun tracking the upper bound, signaling momentum reversal to the upside. Conversely, enter Short positions or exit existing Longs when the dots shift from green to red (bearish) , confirming negative momentum establishment and downward trend tracking.
The momentum factor dots function as a dynamic momentum-based reference pathway that can be used for position management and risk control. During bullish phases, the dot formation represents a momentum-weighted support zone where pullbacks may find stability before continuation. During bearish trends, it acts as resistance where rallies may encounter selling pressure. Price action relative to the momentum factor pathway provides context on trend health: sustained price movement in the direction of the trend (above the dots during bullish phases, below during bearish phases) confirms momentum persistence, while repeated violations may suggest weakening directional conviction.
▶ Configure alert notifications to monitor trend changes without continuous chart observation. The indicator provides three alert types: "Bullish Momentum Signal" triggers specifically on upward trend reversals, "Bearish Momentum Signal" captures downward momentum shifts, and "Momentum Trend Change" fires on any directional transition. These alerts activate only when the trend state changes from one regime to another, eliminating false triggers from intrabar noise or temporary boundary touches that don't result in confirmed trend reversals.
▶ The indicator also offers six pre-designed color schemes (Classic, Aqua, Cosmic, Ember, Neon, Custom) optimized for various chart backgrounds and visual preferences, ensuring the momentum trend remains clearly visible under different display conditions. The bar coloring feature overlays trend direction directly onto the price candles, providing immediate visual confirmation of the momentum regime without needing to reference the dot pattern position.
🟢 Pro Tips for Trading and Investing
▶ Align the configuration preset with your trading timeframe and objectives: Fast Response settings excel on 1-15 minute charts for scalping and day trading where capturing quick momentum shifts is paramount, though this comes with increased signal frequency and potential whipsaws in ranging conditions. Default parameters suit hourly to daily charts for swing trading, providing balanced responsiveness without excessive noise. Smooth Trend configuration works best on 4-hour to weekly timeframes for position trading and investment analysis, prioritizing trend stability over timing precision and significantly reducing false reversals during consolidation periods.
▶ Context matters significantly for momentum-based systems. The indicator performs optimally during trending market regimes where directional persistence exists and may struggle during sideways consolidation where momentum lacks consistency. Before taking signals, assess the broader market structure: look for established higher highs/higher lows (uptrend) or lower highs/lower lows (downtrend) on higher timeframes to confirm you're trading with the dominant directional bias. During range-bound periods, reduce position sizing or wait for the momentum factor dots to establish a clear directional slope and consistent movement before committing capital.
▶ Layer the momentum factor model with complementary analysis rather than using it in isolation. Combine trend signals with volume confirmation (increasing volume on trend changes suggests institutional participation), key support/resistance levels (signals near major levels carry higher probability), and volatility context (ATR expansion can precede significant moves). Consider the momentum decay parameter's impact: values near 0.85 make the model highly sensitive to recent price action, ideal for fast-moving markets but prone to false signals; values near 0.95 create smoother momentum estimates that better filter noise but may lag major reversals.
▶ Implement dynamic position management using the momentum factor pathway as a trailing reference framework. Rather than placing fixed stops, observe the dot formation's progression: as long as it maintains its directional slope and price respects it as support (bullish) or resistance (bearish), the momentum regime remains intact. Exit or tighten stops when price closes decisively through the momentum factor dots against your position, or when the dot pathway itself flattens (losing slope) indicating momentum exhaustion. For portfolio allocation, scale position sizes based on momentum factor strength, e.g., steeper dot progression angles and faster advancement suggest stronger momentum worthy of larger allocations within your risk parameters.
Session Opening Range Breakout (ORBO)This strategy automates a classic Opening Range Breakout (ORBO) approach: it builds a price range for the first minutes after the market opens, then looks for strong breakouts above or below that range to catch early directional moves.
Concept
The idea behind ORBO is simple:
The first minutes after the session open are often highly informative.
Price forms an “opening range” that acts as a mini support/resistance zone.
A clean breakout beyond this zone can lead to high-momentum moves.
This script turns that logic into a fully backtestable strategy in TradingView.
How the strategy works
Opening Range Session
Default session: 09:30–09:50 (exchange time)
During this window, the script tracks:
orHigh → highest high within the session
orLow → lowest low within the session
This forms your Opening Range for the day.
Breakout Logic (after the window ends)
Once the defined session ends:
Long Entry:
If the close crosses above the Opening Range High (orHigh),
→ strategy.entry("OR Long", strategy.long) is triggered.
Short Entry:
If the close crosses below the Opening Range Low (orLow),
→ strategy.entry("OR Short", strategy.short) is triggered.
Only one opening range per day is considered, which keeps the logic clean and easy to interpret.
Daily Reset
At the start of a new trading day, the script resets:
orHigh := na
orLow := na
A fresh Opening Range is then built using the next session’s 09:30–09:50 candles.
This ensures entries are always based on today’s structure, not yesterday’s.
Visuals & Inputs
Inputs:
Opening range session → default: "0930-0950"
Show OR levels → toggle visibility of OR High / Low lines
Fill range body → optional shaded zone between OR High and OR Low
Chart visuals:
A green line marks the Opening Range High.
A red line marks the Opening Range Low.
Optional yellow fill highlights the entire OR zone.
Background shading during the session shows when the range is currently being built.
These visuals make it easy to see:
Where the OR sits relative to current price
How clean / noisy the breakout was
How often price respects or rejects the opening zone
Backtesting & Optimization
Because this is written as a strategy():
You can use TradingView’s Strategy Tester to view:
Win rate
Net profit
Drawdown
Profit factor
Equity curve
Ideas to experiment with:
Change the session window (e.g., 09:15–09:45, 10:00–10:30)
Apply to different:
Markets: indices, FX, crypto, stocks
Timeframes: 1m / 5m / 15m
Add your own:
Stop Loss & Take Profit levels
Time filters (only trade certain days / times)
Volatility filters (e.g., ATR, range size thresholds)
Higher-timeframe trend filter (e.g., only take longs above 200 EMA)
Kaufman Trend Navigator [QuantAlgo]🟢 Overview
The Kaufman Trend Navigator is an adaptive trend following system that combines efficiency-weighted price smoothing with volatility-adjusted bands to identify and track directional market movements. The indicator dynamically adjusts its sensitivity based on market conditions, becoming more responsive during trending periods and more conservative during consolidation. This dual-layer approach provides traders and investors with a systematic framework for trend identification, entry timing, and risk management across multiple timeframes and asset classes.
🟢 How It Works
The indicator employs an efficiency ratio mechanism that measures the directional movement of price relative to total price volatility over a defined lookback period. This ratio determines the adaptive response rate, allowing the system to distinguish between genuine directional moves and random market noise. When price exhibits strong directional characteristics, the internal smoothing accelerates to track the trend more closely. Conversely, during periods of low efficiency or choppy price action, the smoothing becomes more conservative to filter out false signals.
Volatility bands are constructed using normalized range measurements, creating dynamic upper and lower boundaries around the adaptive trend calculation. These bands expand and contract based on recent market volatility, providing context-dependent thresholds for trend validation. The trend line itself updates through a band-following logic where it tracks the relevant boundary based on the current directional bias, creating a stepping mechanism that maintains trend persistence while allowing for validated reversals.
The visual representation uses a gradient-weighted display to emphasize the primary trend line while maintaining clarity on price charts. Trend direction changes trigger when the internal logic confirms a boundary crossover, generating signals for potential position entries or exits. The system includes preset configurations calibrated for different trading timeframes, from responsive settings for scalping to smoother parameters suited for swing and position trading.
🟢 How to Use It
▶ Enter Long positions when the trend line transitions to Bullish (Green) coloring, which indicates upward directional bias has been established. Conversely, enter Short positions or exit Longs when the trend line shifts to Bearish (Red), which signals confirmed downward momentum.
The trend line itself can be used as dynamic support during uptrends and resistance during downtrends, providing logical areas for position management and stop placement. Price remaining above the line during bullish phases or below during bearish phases can also be used as a confirmation of trend strength and continuation probability.
▶ Built-in alert functionality provides real-time notifications for trend changes without requiring continuous chart monitoring. Configure alerts for Bullish Trend Signal to capture upward reversals, Bearish Trend Signal for downward shifts, or the general Trend Change alert to monitor both directions simultaneously. These alerts trigger only on confirmed trend transitions, reducing noise from intrabar fluctuations.
The indicator also includes six color presets (Classic, Aqua, Cosmic, Ember, Neon, Custom) to optimize visual clarity across different chart themes and lighting conditions. Select presets based on your monitor setup and background preference to ensure immediate trend recognition without visual strain. Bar coloring can be enabled to highlight trend direction directly on the price chart, eliminating the need to reference the trend line position during rapid market analysis.
🟢 Pro Tips for Trading and Investing
▶ Match the preset configuration (or your preferred settings) to your trading timeframe: use Fast Response for intraday charts (1-15 minutes), Default for swing trading (hourly to daily), and Smooth Trend for position trading (4-hour to weekly).
▶ Combine trend signals with volume analysis and market structure to filter lower-probability setups. During sideways markets, expect increased signal frequency with reduced reliability; consider waiting for the trend line to establish a clear slope before committing capital.
▶ Use the trend line as a trailing reference rather than a fixed stop level, allowing normal intrabar volatility while protecting against genuine reversals.
▶ For portfolio management, align position sizing with trend strength by observing the angle and consistency of the trend line progression.
