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.
Pinescript-v6
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
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.
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)
