FluxGate Daily Swing StrategySummary in one paragraph
FluxGate treats long and short as different ecosystems. It runs two independent engines so the long side can be bold when the tape rewards upside persistence while the short side can stay selective when downside is messy. The core reads three directional drivers from price geometry then removes overlap before gating with clean path checks. The complementary risk module anchors stop distance to a higher timeframe ATR so a unit means the same thing on SPY and BTC. It can add take profit breakeven and an ATR trail that only activates after the trade earns it. If a stop is hit the strategy can re enter in the same direction on the next bar with a daily retry cap that you control. Add it to a clean chart. Use defaults to see the intended behavior. For conservative workflows evaluate on bar close.
Scope and intent
• Markets. Large cap equities and liquid ETFs major FX pairs US index futures and liquid crypto pairs
• Timeframes. From one minute to daily
• Default demo in this publication. SPY on one day timeframe
• Purpose. Reduce false starts without missing sustained trends by fusing independent drivers and suppressing activity when the path is noisy
• Limits. This is a strategy. Orders are simulated on standard candles. Non standard chart types are not supported for execution
Originality and usefulness
• Unique fusion. FluxGate extracts three drivers that look at price from different angles. Direction measures slope of a smoothed guide and scales by realized volatility so a point of slope does not mean a different thing on different symbols. Persistence looks at short sign agreement to reward series of closes that keep direction. Curvature measures the second difference of a local fit to wake up during convex pushes. These three are then orthonormalized so a strong reading in one does not double count through another.
• Gates that matter. Efficiency ratio prefers direct paths over treadmills. Entropy turns up versus down frequency into an information read. Light fractal cohesion punishes wrinkly paths. Together they slow the system in chop and allow it to open up when the path is clean.
• Separate long and short engines. Threshold tilts adapt to the skew of score excursions. That lets long engage earlier when upside distribution supports it and keeps short cautious where downside surprise and venue frictions are common.
• Practical risk behavior. Stops are ATR anchored on a higher timeframe so the unit is portable. Take profit is expressed in R so two R means the same concept across symbols. Breakeven and trailing only activate after a chosen R so early noise does not squeeze a good entry. Re entry after stop lets the system try again without you babysitting the chart.
• Testability. Every major window and the aggression controls live in Inputs. There is no hidden magic number.
Method overview in plain language
Base measures
• Return basis. Natural log of close over prior close for stability and easy aggregation through time. Realized volatility is the standard deviation of returns over a moving window.
• Range basis for risk. ATR computed on a higher timeframe anchor such as day week or month. That anchor is steady across venues and avoids chasing chart specific quirks.
Components
• Directional intensity. Use an EMA of typical price as a guide. Take the day to day slope as raw direction. Divide by realized volatility to get a unit free measure. Soft clip to keep outliers from dominating.
• Persistence. Encode whether each bar closed up or down. Measure short sign agreement so a string of higher closes scores better than a jittery sequence. This favors push continuity without guessing tops or bottoms.
• Curvature. Fit a short linear regression and compute the second difference of the fitted series. Strong curvature flags acceleration that slope alone may miss.
• Efficiency gate. Compare net move to path length over a gate window. Values near one indicate direct paths. Values near zero indicate treadmill behavior.
• Entropy gate. Convert up versus down frequency into a probability of direction. High entropy means coin toss. The gate narrows there.
• Fractal cohesion. A light read of path wrinkliness relative to span. Lower cohesion reduces the urge to act.
• Phase assist. Map price inside a recent channel to a small signed bias that grows with confidence. This helps entries lean toward the right half of the channel without becoming a breakout rule.
• Shock control. Compare short volatility to long volatility. When short term volatility spikes the shock gate temporarily damps activity so the system waits for pressure to normalize.
Fusion rule
• Normalize the three drivers after removing overlap
• Blend with weights that adapt to your aggression input
• Multiply by the gates to respect path quality
• Smooth just enough to avoid jitter while keeping timing responsive
• Compute an adaptive mean and deviation of the score and set separate long and short thresholds with a small tilt informed by skew sign
• The result is one long score and one short score that can cross their thresholds at different times for the same tape which is a feature not a bug
Signal rule
• A long suggestion appears when the long score crosses above its long threshold while all gates are active
• A short suggestion appears when the short score crosses below its short threshold while all gates are active
• If any required gate is missing the state is wait
• When a position is open the status is in long or in short until the complementary risk engine exits or your entry mode closes and flips
Inputs with guidance
Setup Long
• Base length Long. Master window for the long engine. Typical range twenty four to eighty. Raising it improves selectivity and reduces trade count. Lowering it reacts faster but can increase noise
• Aggression Long. Zero to one. Higher values make thresholds more permissive and shorten smoothing
Setup Short
• Base length Short. Master window for the short engine. Typical range twenty eight to ninety six
• Aggression Short. Zero to one. Lower values keep shorts conservative which is often useful on upward drifting symbols
Entries and UI
• Entry mode. Both or Long only or Short only
Complementary risk engine
• Enable risk engine. Turns on bracket exits while keeping your signal logic untouched
• ATR anchor timeframe. Day Week or Month. This sets the structural unit of stop distance
• ATR length. Default fourteen
• Stop multiple. Default one point five times the anchor ATR
• Use take profit. On by default
• Take profit in R. Default two R
• Breakeven trigger in R. Default one R
Usage recipes
Intraday trend focus
• Entry mode Both
• ATR anchor Week
• Aggression Long zero point five Aggression Short zero point three
• Stop multiple one point five Take profit two R
• Expect fewer trades that stick to directional pushes and skip treadmill noise
Intraday mean reversion focus
• Session windows optional if you add them in your copy
• ATR anchor Day
• Lower aggression both sides
• Breakeven later and trailing later so the first bounce has room
• This favors fade entries that still convert into trends when the path stays clean
Swing continuation
• Signal timeframe four hours or one day
• Confirm timeframe one day if you choose to include bias
• ATR anchor Week or Month
• Larger base windows and a steady two R target
• This accepts fewer entries and aims for larger holds
Properties visible in this publication
• Initial capital 25.000
• Base currency USD
• Default order size percent of equity value three - 3% of the total capital
• Pyramiding zero
• Commission zero point zero three percent - 0.03% of total capital
• Slippage five ticks
• Process orders on close off
• Recalculate after order is filled off
• Calc on every tick off
• Bar magnifier off
• Any request security calls use lookahead off everywhere
Realism and responsible publication
• No performance promises. Past results never guarantee future outcomes
• Fills and slippage vary by venue and feed
• Strategies run on standard candles only
• Shapes can update while a bar is forming and settle on close
• Keep risk per trade sensible. Around one percent is typical for study. Above five to ten percent is rarely sustainable
Honest limitations and failure modes
• Sudden news and thin liquidity can break assumptions behind entropy and cohesion reads
• Gap heavy symbols often behave better with a True Range basis for risk than a simple range
• Very quiet regimes can reduce score contrast. Consider longer windows or higher thresholds when markets sleep
• Session windows follow the exchange time of the chart if you add them
• If stop and target can both be inside a single bar this strategy prefers stop first to keep accounting conservative
Open source reuse and credits
• No reused open source beyond public domain building blocks such as ATR EMA and linear regression concepts
Legal
Education and research only. Not investment advice. You are responsible for your decisions. Test on history and in simulation with realistic costs
Tìm kiếm tập lệnh với "order"
Scalper Pro Pattern Recognition & Price ActionOVERVIEW
Scalper Pro is a comprehensive multi-timeframe trading indicator that combines Smart Money Concepts (SMC) with traditional technical analysis to provide scalpers and day traders with high-probability entry and exit signals. This indicator integrates multiple analytical frameworks into a unified visual system designed specifically for short-term trading strategies.
ORIGINALITY & PURPOSE
What Makes This Script Original
This script is not a simple mashup of existing indicators. Instead, it represents a carefully orchestrated integration of complementary analytical methods that work together to solve a specific problem: identifying high-probability scalping opportunities in volatile markets.
The unique value proposition:
Adaptive Trend Filtering System - Combines a customized SuperTrend algorithm with dual-period range filters (Cirrus Cloud) and Hull Moving Average trend cloud to create a three-layer trend confirmation system
Smart Money Concepts Integration - Incorporates institutional trading concepts (Order Blocks, Fair Value Gaps, Break of Structure) with retail technical indicators for a complete market structure view
Dynamic Risk Management - Automatically calculates stop-loss and take-profit levels based on ATR volatility, providing objective position sizing
ADX-Based Market Regime Detection - Identifies ranging vs. trending markets through ADX analysis with visual bar coloring to prevent whipsaws during consolidation
Why Combine These Specific Components
Each component addresses a specific weakness in scalping:
SuperTrend provides the primary directional bias but can generate false signals in ranging markets
Range Filters smooth out noise and confirm trend direction, reducing SuperTrend false positives
ADX Analysis prevents trading during low-volatility consolidation when most indicators fail
SMC Elements identify institutional activity zones where price is likely to react strongly
ATR-Based Risk Management adapts position sizing to current volatility conditions
The synergy creates a system where signals are only generated when multiple confirmation layers align, significantly reducing false signals common in single-indicator approaches.
HOW IT WORKS
Core Calculation Methodology
1. SuperTrend Signal Generation
The script uses a modified SuperTrend algorithm with the following calculation:
ATR = Average True Range (default: 10 periods)
Factor = 7 (default sensitivity multiplier)
Upper Band = Source + (Factor × ATR)
Lower Band = Source - (Factor × ATR)
Directional Logic:
When price crosses above SuperTrend → Bullish signal
When price crosses below SuperTrend → Bearish signal
SuperTrend value is plotted as dynamic support/resistance
Key Modification: The sensitivity parameter (nsensitivity * 7) allows users to adjust the aggressiveness of trend detection without changing the core ATR calculation.
2. Range Filter System (Cirrus Cloud)
The Range Filter uses a smoothed range calculation to filter out market noise:
Smooth Range Calculation:
WPER = (Period × 2) - 1
AVRNG = EMA(|Price - Price |, Period)
Smooth Range = EMA(AVRNG, WPER) × Multiplier
Two-Layer System:
Layer 1: 22-period with 6x multiplier (broader trend)
Layer 2: 15-period with 5x multiplier (tighter price action)
Visual Output: The space between these two filters is colored:
Green fill = Bullish trend (Layer 1 > Layer 2)
Red fill = Bearish trend (Layer 1 < Layer 2)
This creates a "cloud" that expands during strong trends and contracts during consolidation.
3. ADX Market Regime Detection
Calculation:
+DM = Positive Directional Movement
-DM = Negative Directional Movement
True Range = RMA of True Range (15 periods)
+DI = 100 × RMA(+DM, 15) / True Range
-DI = 100 × RMA(-DM, 15) / True Range
ADX = 100 × RMA(|+DI - -DI| / (+DI + -DI), 15)
Threshold System:
ADX < Threshold (default 15) = Ranging market → Bar color changes to purple
ADX > Threshold = Trending market → Normal bar coloring applies
Purpose: This prevents taking trend-following signals during sideways markets where most indicators produce whipsaws.
4. Smart Money Concepts (SMC) Integration
Order Blocks (OB):
Identified using swing high/low detection with customizable pivot length
Bullish OB: Last down-close candle before bullish Break of Structure (BOS)
Bearish OB: Last up-close candle before bearish BOS
Extended forward until price breaks through them
Fair Value Gaps (FVG):
Detected when a three-candle gap exists:
Bullish FVG: Low > High
Bearish FVG: High < Low
Filtered by price delta percentage to ensure significant gaps
Displayed as boxes that delete when price fills the gap
Break of Structure (BOS) vs. Change of Character (CHoCH):
BOS = Price breaks the previous structural high/low in the current trend direction
CHoCH = Price breaks structure in the opposite direction (potential trend reversal)
Both internal (minor) and swing (major) structures are tracked
Equal Highs/Lows (EQH/EQL):
Detected when consecutive swing highs/lows are within ATR threshold
Often indicates liquidity pools that price may sweep before reversing
5. ATR-Based Risk Management
Calculation:
ATR Band = ATR(14) × Risk Multiplier (default 3%)
Stop Loss = Entry - ATR Band (for longs) or Entry + ATR Band (for shorts)
Take Profit Levels:
TP1 = Entry + (Entry - Stop Loss) × 1
TP2 = Entry + (Entry - Stop Loss) × 2
TP3 = Entry + (Entry - Stop Loss) × 3
Dynamic Labels: Stop loss and take profit levels are automatically calculated and displayed as labels on the chart when new signals trigger.
6. Hull Moving Average Trend Cloud
HMA = WMA(2 × WMA(Close, Period/2) - WMA(Close, Period), sqrt(Period))
Period = 600 bars (long-term trend)
The HMA provides a smoothed long-term trend reference that's more responsive than traditional moving averages while filtering out short-term noise.
HOW TO USE THE INDICATOR
Entry Signals
Primary Buy Signal:
SuperTrend changes to green (price crosses above)
ADX shows market is NOT ranging (bars are NOT purple)
Price is within or near a bullish Order Block OR bullish FVG
Cirrus Cloud shows green fill (Layer 1 > Layer 2)
Primary Sell Signal:
SuperTrend changes to red (price crosses below)
ADX shows market is NOT ranging
Price is within or near a bearish Order Block OR bearish FVG
Cirrus Cloud shows red fill (Layer 1 < Layer 2)
Confirmation Layers
Higher Probability Trades Include:
Bullish/Bearish BOS in the same direction as signal
Equal highs/lows being swept before entry
Price respecting premium/discount zones (above/below equilibrium)
Multiple timeframe alignment (use MTF settings)
Exit Strategy
The indicator provides three take-profit levels:
TP1: Conservative target (1:1 risk-reward)
TP2: Moderate target (2:1 risk-reward)
TP3: Aggressive target (3:1 risk-reward)
Suggested Exit Approach:
Close 1/3 position at TP1
Move stop to breakeven
Close 1/3 position at TP2
Trail remaining position or exit at TP3
Risk Management
Stop Loss:
Use the ATR-based stop loss level displayed on chart
Alternatively, use percentage-based stop (adjustable in settings)
Never risk more than 1-2% of account per trade
Position Sizing:
Position Size = (Account Risk $) / (Entry Price - Stop Loss Price)
CUSTOMIZABLE SETTINGS
Core Parameters
Buy/Sell Signals:
Toggle signals on/off
Adjust SuperTrend sensitivity (0.5 - 2.0)
Risk Management:
Show/hide TP/SL levels
ATR period (default: 14)
Risk percentage (default: 3%)
Number of decimal places for price labels
Trend Features:
Cirrus Cloud display toggle
Range filter periods (x1, x2, x3, x4)
Hull MA length for trend cloud
Smart Money Concepts:
Order Block settings (swing length, display count)
Fair Value Gap parameters (auto-threshold, extend length)
Structure detection (internal vs swing)
EQH/EQL threshold
ADX Settings:
ADX length (default: 15)
Sideways threshold (10-30, default: 15)
Bar color toggle
Display Options:
Previous day/week/month high/low levels
Premium/Discount/Equilibrium zones
Trend candle coloring (colored or monochrome)
BEST PRACTICES & TRADING TIPS
Optimal Use Cases
Scalping on lower timeframes (1m, 5m, 15m)
Rapid entry/exit with clear TP levels
ADX filter prevents choppy market entries
Day trading on medium timeframes (30m, 1H)
Stronger trend confirmation
Better risk-reward ratios
Swing trading entries on higher timeframes (4H, Daily)
Higher-probability structural setups
Larger ATR-based stops accommodate volatility
Market Conditions
Best Performance:
Trending markets with clear directional bias
Post-news volatility with defined structure
Markets respecting support/resistance levels
Avoid Trading When:
ADX indicator shows purple bars (ranging market)
Multiple conflicting signals across timeframes
Major news events without clear price structure
Low volume periods (market open/close)
Common Mistakes to Avoid
Ignoring the ADX filter - Taking signals during ranging markets leads to whipsaws
Not waiting for confirmation - Enter only when multiple layers align
Overtrading - Fewer high-quality setups outperform many mediocre ones
Ignoring risk management - Always use the calculated stop losses
Fighting the trend - Trade WITH the SuperTrend and Cirrus Cloud direction
TECHNICAL SPECIFICATIONS
Indicator Type: Overlay (plots on price chart)
Calculation Resources:
Max labels: 500
Max lines: 500
Max boxes: 500
Max bars back: 500
Pine Script Version: 5
Compatible Timeframes: All timeframes (optimized for 1m to 1D)
Compatible Instruments:
Forex pairs
Crypto assets
Stock indices
Individual stocks
Commodities
THEORETICAL FOUNDATION
Trend-Following Concepts
This indicator is based on the principle that markets trend more often than they range, and that trends tend to persist. The SuperTrend component captures this momentum while the range filters prevent premature entries during pullbacks.
Smart Money Theory
The SMC elements are based on the concept that institutional traders (banks, hedge funds) leave footprints in the form of:
Order Blocks: Areas where large orders were placed
Fair Value Gaps: Inefficient price movements that may be revisited
Liquidity Sweeps: Stop hunts before continuation (EQH/EQL)
Volatility-Based Position Sizing
Using ATR for stop-loss placement ensures that stop distances adapt to current market conditions:
Tight stops in low volatility (avoids excessive risk)
Wider stops in high volatility (avoids premature stop-outs)
PERFORMANCE EXPECTATIONS
Realistic Expectations
Win Rate:
Expected: 45-55% (trend-following systems rarely exceed 60%)
Higher win rates on trending days
Lower win rates during consolidation (even with ADX filter)
Risk-Reward Ratio:
Target: 1.5:1 minimum (TP2)
Achievable: 2:1 to 3:1 on strong trends
Drawdowns:
Normal: 10-15% of account during choppy periods
Maximum: Should not exceed 20% with proper risk management
Optimization Tips
Backtesting Recommendations:
Test on at least 1 year of historical data
Include different market conditions (trending, ranging, volatile)
Adjust SuperTrend sensitivity per instrument
Optimize ADX threshold for your specific market
Record trades to identify personal execution errors
FREQUENTLY ASKED QUESTIONS
Q: Can I use this for automated trading?
A: The indicator provides signals, but you'll need to code a strategy script separately for automation. The signals can trigger alerts that connect to trading bots.
Q: Why do I see conflicting signals?
A: This is normal during transition periods. Wait for all confirmation layers to align before entering.
Q: How often should I expect signals?
A: Depends on timeframe and market conditions. On 5m charts during trending markets: 3-7 quality setups per session.
Q: Can I use only some features?
A: Yes, all components can be toggled on/off. However, the system works best with all confirmations active.
Q: What's the difference between internal and swing structures?
A: Internal = minor price structures (smaller pivots). Swing = major price structures (larger pivots). Both provide different levels of confirmation.
DISCLAIMER
This indicator is a tool for technical analysis and should not be the sole basis for trading decisions. Past performance does not guarantee future results. Always:
Use proper risk management
Test on demo accounts first
Never risk more than you can afford to lose
Combine with fundamental analysis when applicable
Understand that no indicator is 100% accurate
License: Mozilla Public License 2.0
Author: DrFXGOD
VERSION HISTORY & UPDATES
Initial Release - Version 1.0
Integrated SuperTrend, Range Filters, ADX, SMC concepts
ATR-based risk management
Multi-timeframe support
Customizable visual elements
SUPPORT & DOCUMENTATION
For questions, suggestions, or bug reports, please comment on the script page or contact the author through TradingView.
Additional Resources:
Smart Money Concepts: Research ICT (Inner Circle Trader) materials
ATR and Volatility: Refer to Wilder's original ATR documentation
SuperTrend Indicator: Study original SuperTrend strategy papers
Smart Structure Pro - Market Structure & Smart Money Concepts═══════════════════════════════════════════════════════════════════════════════
SMART STRUCTURE PRO
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A comprehensive market structure analysis tool that identifies institutional trading
patterns and smart money concepts for improved trade timing and decision-making.
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📊 WHAT IT DOES
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This indicator automatically detects and visualizes key market structure elements:
🔹 BOS (Break of Structure)
- Identifies trend continuation patterns
- Marks when price breaks above previous highs (bullish) or below previous lows (bearish)
- Confirms trend strength and momentum
🔹 CHoCH (Change of Character)
- Detects potential trend reversals
- Alerts when market structure shifts from bullish to bearish or vice versa
- Helps identify early reversal opportunities
🔹 Order Blocks
- Highlights institutional entry zones
- Identifies the last opposite candle before a structure break
- Shows areas where smart money likely entered positions
🔹 Fair Value Gaps (FVG)
- Detects price imbalances and inefficiencies
- Shows areas where price moved rapidly leaving gaps
- Often act as support/resistance when retested
🔹 Liquidity Zones
- Marks swing high and low levels
- Identifies areas where stop losses likely cluster
- Shows potential stop hunt and liquidity grab zones
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🎯 HOW TO USE
═══════════════════════════════════════════════════════════════════════════════
BULLISH SETUP:
1. Wait for Bullish CHoCH (trend reversal signal) or BOS ↑ (continuation)
2. Look for price to pull back into an Order Block or Fair Value Gap
3. Enter long when price bounces from these zones
4. Place stop loss below the Order Block
5. Target the next liquidity zone or resistance level
BEARISH SETUP:
1. Wait for Bearish CHoCH (trend reversal signal) or BOS ↓ (continuation)
2. Look for price to retrace into an Order Block or Fair Value Gap
3. Enter short when price rejects from these zones
4. Place stop loss above the Order Block
5. Target the next liquidity zone or support level
DASHBOARD INTERPRETATION:
• Trend: Current market direction (Bullish/Bearish)
• Volume: Confirmation strength (High volume = stronger signals)
• Signal: Latest structure break detected
• Key High/Low: Critical levels for the current trend
• Position: Price location (Premium = expensive, Discount = cheap)
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⚙️ SETTINGS GUIDE
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STRUCTURE DETECTION:
• Pivot Length (Default: 10)
- Lower values = More signals but potentially weaker
- Higher values = Fewer signals but stronger/more reliable
- Recommended: 8-12 for intraday, 10-15 for higher timeframes
• Structure Line Extension
- Visual preference for how far lines extend
- Does not affect signal detection
SMART MONEY CONCEPTS:
• Order Block Extension: How long OB boxes remain visible
• FVG Extension: How long gap boxes remain visible
• Min FVG Size: Filter out small gaps (0 = show all)
- Set to 10-20% to reduce noise
- Set to 0 to see all gaps
VOLUME FILTER:
• Volume Confirmation (Recommended: ON)
- Filters weak signals without volume support
- Reduces false breakouts
• Volume Multiplier (Default: 1.5)
- Higher = Stricter filtering (fewer but stronger signals)
- Lower = More signals (but may include weak ones)
DISPLAY:
• Dashboard: Toggle information panel
• Trend Background: Subtle color tint showing current trend
• Dashboard Position: Choose corner placement
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🔔 ALERTS
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Available alert conditions:
✓ Bullish BOS - Uptrend continuation confirmed
✓ Bearish BOS - Downtrend continuation confirmed
✓ Bullish CHoCH - Reversal to uptrend detected
✓ Bearish CHoCH - Reversal to downtrend detected
✓ Structure Break - Any significant market structure change
To set up alerts:
1. Click the "⏰" alert icon
2. Select "Smart Structure Pro"
3. Choose your desired condition
4. Configure notification method
5. Click "Create"
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⚠️ IMPORTANT DISCLOSURES
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REPAINTING BEHAVIOR:
• Pivot points WILL repaint until confirmed (this is by design and unavoidable)
• Structure breaks (BOS/CHoCH) use CLOSED candles and do NOT repaint after confirmation
• Order Blocks and FVGs are drawn on confirmed signals and do NOT repaint
• All signals wait for candle close before triggering
BEST PRACTICES:
• Use on higher timeframes (15min+) for more reliable signals
• Combine with other analysis (support/resistance, volume profile, etc.)
• Wait for candle close confirmation before acting on signals
• Use proper risk management - this is not a standalone trading system
• Backtest on your preferred instrument and timeframe
PERFORMANCE:
• Limited to 100 boxes, 100 lines, 100 labels for optimal performance
• Older objects automatically removed as new ones appear
• Works on all markets (Forex, Crypto, Stocks, Indices, Commodities)
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📚 CONCEPTS EXPLAINED
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MARKET STRUCTURE:
Market structure refers to the pattern of price movements creating swing highs
and lows. Understanding structure helps identify trend direction and potential
reversal points.
SMART MONEY CONCEPTS:
These are trading techniques based on tracking institutional order flow and
understanding where large players (banks, funds, institutions) enter and exit
positions.
ORDER BLOCKS:
The last opposing candle before a strong directional move. Institutions often
leave unfilled orders in these zones, which can act as support/resistance when
price returns.
FAIR VALUE GAPS:
Areas where price moved so quickly that it left an imbalance. These gaps often
get "filled" as price returns to find equilibrium, creating trading opportunities.
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🎓 EDUCATIONAL VALUE
═══════════════════════════════════════════════════════════════════════════════
This indicator helps traders:
✓ Understand market structure mechanics
✓ Identify institutional trading patterns
✓ Improve trade timing and entry precision
✓ Recognize trend continuation vs reversal
✓ Learn smart money concepts through visualization
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📋 TECHNICAL DETAILS
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• Version: 1.0.0
• Pine Script Version: 5
• Indicator Type: Overlay
• No Repainting: Structure breaks use confirmed candles
• Performance Optimized: Limited drawing objects
• Works On: All markets and timeframes
• Alerts: Yes, fully customizable
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👤 AUTHOR
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Created by: Zakaria Safri
Original Work: All code and concepts are original implementations
Based On: ICT (Inner Circle Trader) educational concepts
License: © 2024 Zakaria Safri - Personal Use Only
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⚖️ DISCLAIMER
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This indicator is for educational and informational purposes only. It does not
constitute financial advice. Trading involves substantial risk of loss. Past
performance does not guarantee future results. Always conduct your own research
and consult with a licensed financial advisor before making trading decisions.
The author is not responsible for any losses incurred from using this indicator.
═══════════════════════════════════════════════════════════════════════════════
If you find this indicator helpful, please:
👍 Like and favorite
⭐ Leave a review
📢 Share with other traders
💬 Comment with feedback or suggestions
Happy Trading! 📈
First-Move-Wrong Toolkit [CHE] First-Move-Wrong Toolkit — Session-bound sweep rejection with structure confirmation
Summary
This indicator marks potential “first move wrong” reversals during a defined trading session. It looks for a quick sweep beyond the prior day high or low, or the opening range high or low, followed by rejection and a basic structure confirmation. Optional rules require a retest and a VWAP reclaim in the direction of the trade idea. The script renders session levels as right-extended lines, signals as labels, optional SL/TP guide lines for visualization, and background tints during sweep events. Pivots are confirmed using swing width, which reduces repaint risk compared to live swings.
Motivation: Why this design?
Intraday reversals often start with a liquidity sweep around obvious highs or lows. Acting on the sweep alone can be noisy, while waiting for structure break and a retest can be slow. This tool balances both by checking a sweep and rejection at session-relevant levels, then requiring a simple structure cue and, optionally, a retest and a VWAP filter. The goal is a clear, rule-based signal layer that is easy to audit on chart without hidden state.
What’s different vs. standard approaches?
Baseline reference: Simple sweep detectors or basic CHOCH markers that ignore session context and liquidity anchors.
Architecture differences:
Session-aware opening range tracking that finalizes after the chosen minutes from session start.
Daily previous high and low pulled without lookahead, then extended forward as visual anchors.
Confirmed pivot highs and lows to avoid repaint from live, unconfirmed swings.
Optional retest rule using crossover or crossunder at the trigger level.
Optional VWAP filter to demand reclaim in the intended direction.
Global label cooldown to prevent clusters of signals.
Practical effect: Fewer one-off flips around noisy levels, clearer alignment with session structure, and compact visual feedback through lines, labels, and tints.
How it works (technical)
Levels: During the defined session, the script builds an opening range high and low until the configured minute mark after session start, then freezes those levels for the day. It also fetches the previous day high and low from the daily timeframe without lookahead and extends them forward.
Sweep and rejection: A sweep is defined as price moving beyond a target level and then rejecting back inside on the same bar. The script checks this condition separately for highs and lows against opening range and previous-day levels.
Structure validation: Confirmed pivot highs and lows are computed using a symmetric swing width. A bearish idea requires a prior sweep of a high plus a break through the last confirmed swing low. A bullish idea requires a prior sweep of a low plus a break through the last confirmed swing high.
Optional retest: If enabled, a bearish signal needs a cross under the bearish trigger level; a bullish signal needs a cross over the bullish trigger level.
VWAP filter (optional): The script requires a reclaim of VWAP in the intended direction when enabled.
State handling: Opening range values, previous-day lines, and the label cooldown timestamp are stored in persistent variables. Lines are created once and updated each bar to extend forward.
Repaint considerations: Pivots confirm only after the specified swing width, reducing repaint. The daily level request is performed without lookahead. Signals use closed-bar checks implied by crossover and crossunder logic.
Parameter Guide
Session (local) — Defines the active trading window. Default nine to seventeen. Narrower windows focus on the main session drive.
Opening Range (min) — Minutes from session start to finalize OR levels. Default fifteen. Shorter values react faster; longer values stabilize levels.
Use PrevDay H/L levels — Toggle previous-day anchors. On by default.
Use OR H/L levels — Toggle opening range anchors. On by default.
Equal H/L tolerance (ticks) — Intended tolerance for equal highs or lows. Default one. (Unknown/Optional) in current signals.
Swing width — Bars on both sides for confirmed pivots. Default two. Larger values reduce noise but confirm later.
Require CHOCH after sweep — Enforces structure break after a sweep. On by default.
Prefer retest entries — Requires crossover or crossunder of the trigger level. On by default.
VWAP filter — Demands a reclaim of VWAP in signal direction. Off by default.
TP in R (guide) — Multiplier for visual TP guides. Default one. Visualization only.
Show levels / Show signals / Show R-guides — Rendering toggles. R-guides are visual aids, not orders.
Label cooldown (bars) — Minimum bars between labels. Default five. Higher values reduce clusters.
Palette inputs — Colors and transparencies for levels, labels, VWAP, and tints.
Reading & Interpretation
Lines: Dotted lines represent opening range high and low after the OR window completes. Dashed lines represent previous-day high and low.
Signals: “Long” labels appear after a low-side sweep with rejection and structure confirmation, subject to optional retest and VWAP rules. “Short” labels mirror this on the high side.
Background tints: Red-tinted bars indicate a high-side sweep and rejection. Green-tinted bars indicate a low-side sweep and rejection.
R-guides: Circles display a visual stop level at the bar extreme and a target guide based on the selected multiple. They are informational only.
Practical Workflows & Combinations
Session reversal scans: During the first hour, watch for sweeps around previous-day or opening range levels, then wait for structure confirmation and optional retest.
Trend following with filters: Combine signals with higher-timeframe structure or a moving average regime check. Ignore signals against the dominant regime.
Exits and stops: Use the visual stop as a reference near the sweep extreme; adapt the target guide to volatility and market conditions.
Multi-asset / Multi-TF: Works on intraday timeframes for liquid futures, indices, forex, and large-cap equities. Start with default settings and adjust swing width and OR minutes to instrument volatility.
Behavior, Constraints & Performance
Repaint/confirmation: Pivots confirm after the swing window completes. Signals occur only when conditions are met on closed bars.
security()/HTF: Daily previous-day levels are requested without lookahead to reduce repaint.
Resources: Uses persistent variables and line updates per bar; no heavy loops or arrays.
Known limits: Signals can arrive later when swing width is large. Gaps around session boundaries may distort OR levels. VWAP behavior may vary with partial sessions or illiquid assets.
Sensible Defaults & Quick Tuning
Starting point: Session nine to seventeen, opening range fifteen minutes, swing width two, CHOCH required, retest on, VWAP off, cooldown five bars.
Too many flips: Increase swing width, enable VWAP filter, or raise label cooldown.
Too sluggish: Reduce swing width or shorten the opening range window.
Too many session-level hits: Disable either previous-day levels or opening range levels to simplify context.
What this indicator is—and isn’t
This is a session-aware visualization and signal layer focused on sweep-plus-structure behavior. It is not a complete trading system and does not manage orders, risk, or portfolio exposure. Use it with market structure, risk limits, and execution rules that fit your process.
Disclaimer
The content provided, including all code and materials, is strictly for educational and informational purposes only. It is not intended as, and should not be interpreted as, financial advice, a recommendation to buy or sell any financial instrument, or an offer of any financial product or service. All strategies, tools, and examples discussed are provided for illustrative purposes to demonstrate coding techniques and the functionality of Pine Script within a trading context.
Any results from strategies or tools provided are hypothetical, and past performance is not indicative of future results. Trading and investing involve high risk, including the potential loss of principal, and may not be suitable for all individuals. Before making any trading decisions, please consult with a qualified financial professional to understand the risks involved.
By using this script, you acknowledge and agree that any trading decisions are made solely at your discretion and risk.
Do not use this indicator on Heikin-Ashi, Renko, Kagi, Point-and-Figure, or Range charts, as these chart types can produce unrealistic results for signal markers and alerts.
Best regards and happy trading
Chervolino
Options levelsOverview
Options Levels 🎯 plots 13 key institutional and options-based levels directly on your chart — including Call Wall, Put Wall, Gamma Flip, Whales Pivot, five Whale levels, and Sigma deviation bands (σ¹ / σ²).
It’s designed for both intraday and swing traders, offering a clean visual structure with elegant emoji labels, flexible visibility controls, and precise right-edge extensions for each line.
✨ Key Features
Single structured input with 13 ordered levels:
CallWall, PutWall, GammaFlip, Whales Pivot, Whale1..Whale5, Upperσ1, Upperσ2, Lowerσ1, Lowerσ2
Expressive emoji labels (🟢, 🔴, ⚖️, 🌑, 🐋, σ¹/σ²) optimized for dark themes.
Right-edge alignment: each line extends exactly to its label — no infinite lines.
Group visibility toggles:
• Critical Levels → Call Wall, Put Wall, Gamma Flip, Whales Pivot
• Whale Levels → Whale 1–5
• Sigma Bands → Upper/Lower σ¹ and σ²
Dynamic line-length multipliers that emphasize key levels.
Built-in alert conditions:
• Price crossing above the Call Wall
• Price crossing below the Put Wall
⚙️ Inputs & Settings
📋 Level List (string) : comma-separated list of 13 numeric values.
Example:
🎨 Appearance
• Base line length (bars)
• Label visibility toggle
• Line thickness
• Extend line and label to the right
• Distance (bars) between last candle and label
👁️ Visibility Controls
• Toggle Critical, Whale, or Sigma levels independently
🚀 How to Use
Paste your list of 13 ordered levels into the input field.
Adjust base length and thickness according to your timeframe.
Enable “Extend to the right” to position labels neatly beyond the last candle.
Use visibility toggles to focus on specific level groups (e.g., hide Whale Levels for short-term setups).
Optionally enable alerts to track price breakouts above/below Call and Put Walls.
The plotted levels are derived from aggregated options flow data, institutional positioning, and volatility-based deviations (σ). They serve as reference zones rather than predictive signals, helping visualize where liquidity and dealer hedging pressure may cluster.
📖 Level Definitions
Call Wall 🟢 — The strike with the highest call open interest; potential resistance area.
Put Wall 🔴 — The strike with the highest put open interest; potential support area.
Gamma Flip ⚖️ — Level where total gamma exposure changes sign; may reflect a shift in dealer hedging behavior.
Whales Pivot 🌑 — Represents the average institutional positioning from the previous trading day, reflecting where large option flows were most concentrated.
Whale Levels 🐋 — High-premium or large-volume strikes typically linked to institutional activity.
Upper σ¹ / σ² 📈 — One and two standard deviations above spot; potential overextension zones.
Lower σ¹ / σ² 📉 — One and two standard deviations below spot; potential mean-reversion zones.
Levels are manually input by the user. This script is a visual reference, not a predictive model.
⚠️ Notes
Levels are user-provided (not calculated by this script).
The indicator does not issue buy/sell signals or provide performance guarantees.
Designed purely as a visual aid for contextual market reference.
Optimized with barstate.islast for performance (draws only at the latest bar).
Disclaimer:
This indicator is for educational and visual purposes only. It does not generate buy/sell signals or guarantee future results. User-provided levels are meant for contextual reference only.
Developed for traders who rely on market structure and options flow context. Feedback and suggestions are welcome.
Smart Money Concept v1Smart Money Concept Indicator – Visual Interpretation Guide
What Happens When Liquidity Lines Are Broken
🟩 Green Line Broken (Buy-Side Liquidity Pool Swept)
- Indicates price has dipped below a previous swing low where sell stops are likely placed.
- Market Makers may be triggering these stops to accumulate long positions.
- Often followed by a bullish reversal.
- Trader Actions:
• Look for a bullish candle close after the sweep.
• Confirm with nearby Bullish Order Block or Fair Value Gap.
• Consider entering a Buy trade (SLH entry).
- If price continues falling: Indicates trend continuation and invalidation of the buy-side liquidity zone.
🟥 Red Line Broken (Sell-Side Liquidity Pool Swept)
- Indicates price has moved above a previous swing high where buy stops are likely placed.
- Market Makers may be triggering these stops to accumulate short positions.
- Often followed by a bearish reversal.
- Trader Actions:
• Look for a bearish candle close after the sweep.
• Confirm with nearby Bearish Order Block or Fair Value Gap.
• Consider entering a Sell trade (SLH entry).
- If price continues rising: Indicates trend continuation and invalidation of the sell-side liquidity zone.
Chart-Based Interpretation of Green Line Breaks
In the provided DOGE/USD 15-minute chart image:
- Green lines represent buy-side liquidity zones.
- If these lines are broken:
• It may be a stop hunt before a bullish continuation.
• Or a false Break of Structure (BOS) leading to deeper retracement.
- Confirmation is needed from candle structure and nearby OB/FVG zones.
Is the Pink Zone a Valid Bullish Order Block?
To validate the pink zone as a Bullish OB:
- It should be formed by a strong down-close candle followed by a bullish move.
- Price should have rallied from this zone previously.
- If price is now retesting it and showing bullish reaction, it confirms validity.
- If formed during low volume or price never rallied from it, it may not be valid.
Smart Money Concept - Liquidity Line Breaks Explained
This document explains how traders should interpret the breaking of green (buy-side) and red (sell-side) liquidity lines when using the Smart Money Concept indicator. These lines represent key liquidity pools where stop orders are likely placed.
🟩 Green Line Broken (Buy-Side Liquidity Pool Swept)
When the green line is broken, it indicates:
• - Price has dipped below a previous swing low where sell stops were likely placed.
• - Market Makers have triggered those stops to accumulate long positions.
• - This is often followed by a bullish reversal.
Trader Actions:
• - Look for a bullish candle close after the sweep.
• - Confirm with a nearby Bullish Order Block or Fair Value Gap.
• - Consider entering a Buy trade (SLH entry).
🟥 Red Line Broken (Sell-Side Liquidity Pool Swept)
When the red line is broken, it indicates:
• - Price has moved above a previous swing high where buy stops were likely placed.
• - Market Makers have triggered those stops to accumulate short positions.
• - This is often followed by a bearish reversal.
Trader Actions:
• - Look for a bearish candle close after the sweep.
• - Confirm with a nearby Bearish Order Block or Fair Value Gap.
• - Consider entering a Sell trade (SLH entry).
📌 Additional Notes
• - If price continues beyond the liquidity line without reversal, it may indicate a trend continuation rather than a stop hunt.
• - Always confirm with Higher Time Frame bias, Institutional Order Flow, and price reaction at the zone.
LibbyThis script is a refined chopzone index script with additional functionalities.
it produce buy and sell signals as directed by chopzone
How to use:
BUY: Look for buy signal on the chart and proceed to place buy or long orders
SELL: Look for sell on the chart and proceed to place sell or short orders.
NOTE: i recommend you set alerts and make it activate on bar close to avoid fadeouts and sideways.
expect sideways market and multiple opposite signals within a short time during news or when economic data are released.
as always, no indicator is failproof, it is recommended to always pair more than 1 indicator for more clarity and practice safe trading.
KCandle Strategy 1.0# KCandle Strategy 1.0 - Trading Strategy Description
## Overview
The **KCandle Strategy** is an advanced Pine Script trading system based on bullish and bearish engulfing candlestick patterns, enhanced with sophisticated risk management and position optimization features.
## Core Logic
### Entry Signal Generation
- **Pattern Recognition**: Detects bullish and bearish engulfing candlestick formations
- **EMA Filter**: Uses a customizable EMA (default 25) to filter trades in the direction of the trend
- **Entry Levels**:
- **Long entries** at 25% of the candlestick range from the low
- **Short entries** at 75% of the candlestick range from the low
- **Signal Validation**: Orange candlesticks indicate valid setup conditions
### Risk Management System
#### 1. **Stop Loss & Take Profit**
- Configurable stop loss in pips
- Risk-reward ratio setting (default 2:1)
- Visual representation with colored lines and labels
#### 2. **Break-Even Management**
- Automatically moves stop loss to break-even when specified R:R is reached
- Customizable break-even offset for added protection
- Prevents losing trades after reaching profitability
#### 3. **Trailing Stop System**
- **Activation Trigger**: Activates when position reaches specified R:R level
- **Distance Control**: Maintains trailing stop at defined distance from entry
- **Step Management**: Moves stop loss forward in incremental R steps
- **Dynamic Protection**: Locks in profits while allowing for continued upside
### Advanced Features
#### Position Management
- **Pyramiding Support**: Optional multiple position entries with size reduction
- **Order Expiration**: Pending orders automatically cancel after specified bars
- **Position Sizing**: Percentage-based allocation with pyramid level adjustments
#### Visual Interface
- **Real-time Monitoring**: Comprehensive information panel with all strategy metrics
- **Historical Tracking**: Visual representation of past trades and levels
- **Color-coded Indicators**: Different colors for break-even, trailing, and standard stops
- **Debug Options**: Optional labels for troubleshooting and optimization
## Key Parameters
### Basic Settings
- **EMA Length**: Trend filter period
- **Stop Loss**: Risk per trade in pips
- **Risk/Reward**: Target profit ratio
- **Order Validity**: Duration of pending orders
### Risk Management
- **Break-Even R:R**: Profit level to trigger break-even
- **Trailing Activation**: R:R level to start trailing
- **Trailing Distance**: Stop distance from entry when trailing
- **Trailing Step**: Increment for stop loss advancement
## Strategy Benefits
1. **Objective Entry Signals**: Based on proven candlestick patterns
2. **Trend Alignment**: EMA filter ensures trades align with market direction
3. **Robust Risk Control**: Multiple layers of protection (SL, BE, Trailing)
4. **Profit Optimization**: Trailing stops maximize winning trade potential
5. **Flexibility**: Extensive customization options for different market conditions
6. **Visual Clarity**: Complete visual feedback for trade management
## Ideal Use Cases
- **Swing Trading**: Medium-term positions with trend-following approach
- **Breakout Trading**: Capturing momentum from engulfing patterns
- **Risk-Conscious Trading**: Suitable for traders prioritizing capital preservation
- **Multi-Timeframe**: Adaptable to various timeframes and instruments
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*The KCandle Strategy combines traditional technical analysis with modern risk management techniques, providing traders with a comprehensive tool for systematic market participation.*
Round Levels (.000 endings)his indicator automatically detects and marks horizontal price levels that end with trailing zeros (psychological round numbers). Examples: 1.17000, 1.16900, 1.16800 etc. These levels often act as strong support or resistance zones because traders and institutions tend to place orders around round numbers.
Features:
Plots horizontal lines at configurable “round” intervals (e.g., .000, .050, .500).
Option to select how many levels above and below current price to display.
Labels each level with its exact price for easy identification.
Helps visualize psychological levels, institutional zones, and round-number trading strategies.
Use Cases:
Spotting potential reversal zones where many traders cluster orders.
Enhancing confluence with other tools (support/resistance, Fibonacci, supply/demand).
Works on all assets (Forex, Stocks, Crypto, Indices) and all timeframes.
Grand Slam Risk ManagementGrand Slam Risk Management (GSRM) Indicator
OVERVIEW
The Grand Slam Risk Management Indicator transforms complex position sizing calculations into real-time, visual risk metrics—enabling disciplined trading decisions without the emotional guesswork that destroys accounts. This comprehensive tool is designed for active day traders and swing traders who want to automate critical risk management calculations directly on their TradingView charts. 🚀
THE GRAND SLAM RISK MANAGEMENT STRATEGY
Core Philosophy
The Grand Slam Risk Management Strategy (GSRM) gets its name from baseball's ultimate scoring play: a grand slam can only be hit when three runners are already on base, requiring at least three prior successful at-bats (hits or walks) to create the opportunity. This perfectly embodies the GSRM philosophy—consistent "base hits" in trading create the foundation for larger wins while protecting your account from devastating losses. Just as baseball teams win championships through disciplined, consistent play rather than swinging for the fences every at-bat, successful traders build wealth through reliable, repeatable profits rather than chasing home runs that often result in strikeouts. ⚾
Strategy Framework
Capital Allocation : 💰
• Working Balance: Account balance minus PDT requirement ($25,000 minimum for margin accounts)
• Allocated Buying Power: Working balance × leverage (4:1 for day trading, 2:1 for swing, 1:1 for cash)
• Daily Profit Target: 5% of allocated buying power (default)
The Base Hit System : 🎯
• Daily profit target divided into 4 "base hits"
• Each base hit represents 25% of daily goal
• Max risk per trade: 50% of base hit target (maintains 2:1 reward/risk minimum)
• Daily max loss: 2 base hits (recoverable with 2 winning trades)
Three-Tier Profit Structure : 🚀
• Tier 1 (5%): Minimum acceptable profit - "Why else take the trade?"
• Tier 2 (10%): Solid win - the target "base hit"
• Tier 3 (20%): Home run - when momentum is strongly in your favor 🏠🏃
Position Sizing Levels : 📊
• Quarter Position (25% of max): Testing the waters, lower conviction setups
• Half Position (50% of max): Standard confidence trades
• Max Position (100%): High conviction, ideal setup conditions
INDICATOR FEATURES
Real-Time Calculations ⚡
• Dynamic Position Sizing: Automatically calculates share quantities based on account balance and current price
• Profit & Loss Targets: Displays dollar amounts for profit targets and stop-losses across all position sizes
• Risk Metrics: Shows daily profit goals, max loss thresholds, and P&L ratios
Advanced Stop-Loss Methods 🛡️
1. Percentage-Based Stops : Fixed 50% of profit target (maintains 2:1 reward/risk)
2. ATR-Based Stops : Dynamic stops that adapt to market volatility using Average True Range (ATR)
• Tier 1: 0.5× ATR (tight/scalping)
• Tier 2: 1.0× ATR (standard)
• Tier 3: 1.5× ATR (wide/trending)
Cost Basis Options 📈
• Last Close: Uses previous bar's closing price for stable calculations
• VWMA: Volume-Weighted Moving Average (default: 9) estimate cost-basis from recent volume-weighted price action
• SMA/EMA: Use Simple or Exponential Moving Average (default: 9) useful for planning entries at SMA/EMA cross-overs and bounces.
• VWAP: Volume-Weighted Average Price (default: daily) for entry point planning at bounce or break of VWAP.
* Ask/Bid: Entry point calculations based on current Ask or Bid price (only available on 1T charts)
Visual Risk Management 🔑
• Color-Coded P&L Ratio :
- Green (≤0.5): Conservative, favorable risk ✅
- Yellow (0.5-1.0): Balanced risk ⚠️
- Red (>1.0): Aggressive, requires higher win rate 🛑
• Position Size Color Coding : Green (quarter) → Yellow (half) → Red (max) for quick risk assessment
HOW TO USE THE GSRM INDICATOR
Initial Setup (One-Time Configuration) ⚙️
1. Set Account Balance: Enter your total trading account value
2. Configure PDT Protection: Enable for margin accounts ≥$25,000 to protect required funds
3. Select Leverage: 4:1 (day trading), 2:1 (swing), or 1:1 (cash account)
4. Adjust Risk Percentage: Default 5% of allocated buying power; reduce for conservative approach
Trading Workflow
Pre-Market Preparation: 🌅
1. Review daily profit target and max loss displayed in green/red
2. Note your base hit target - this is your standard trade goal
3. Check P&L ratio - ensure it's sustainable for your win rate
Trade Execution: 🚀
1. Assess Setup Quality :
• Strong setup → Consider half or max position 💪
• Decent setup → Quarter or half position 👍
• Testing idea → Quarter position only 🧪
2. Select Profit Tier Based on Market Conditions :
• Choppy market → Target Tier 1 (5%) 🌊
• Normal conditions → Target Tier 2 (10%) ➡️
• Strong momentum → Target Tier 3 (20%) 🚀
3. Choose Stop Method :
• Percentage stops: Best for stocks with clear support/resistance
• ATR stops: Better for volatile stocks or news-driven trades. WARNING: this may result in tighter stops, negatively affecting your P&L. To offset this effect, try increasing the number of base hits to achieve your daily profit target and recover from a daily max loss. Be sure the resultant P&L ratio is in the conservative range ≤0.5. This will allow you to adjust your per-trade P&L targets without reducing your daily profit target or increasing your max risk.
4. Execute Using Table Values :
• 🔎 Find your position size group (🟢quarter/🟡half/🔴max)
• 🔎 Find your profit target row (5%/10%/20%) for your position size group
• ⚠️ Do not exceed the share count and stop-loss values displayed ⚠️
Risk Management Rules 🛡️
Daily Limits : 🚨
• Stop trading after hitting daily max loss (prevent tilt/revenge trading)
• Stop trading when a low-risk, minimum-loss trade would exceed your daily max loss (prevent exceeding max)
• Stop trading if you fall below the Daily Profit Target after having achieved it (prevent tilt/revenge trading)
• Cold Market: Stop trading after reaching daily profit target (preserve gains) ❄️
• Hot Market: Three Strikes - stop trading after 3 total max loss trades in a day (prevent tilt/revenge trading) 🔥
Position Management : 📏
• Never exceed max position size shown (protects from overleverage)
• Use quarter positions when daily P&L is negative or below first profit goal (40% of target)
• Use half positions only while daily P&L is above first profit goal (40% of target)
• Use full positions only while daily P&L is above profit goal (100% of target)
A/B Testing Features 🧪
Stop-Loss Methods :
• Week 1: Use percentage-based stops
• Week 2: Use ATR-based stops
• Compare win rates and average losses to optimize
Cost Basis Models :
Pick the highest probable cost-basis and keep your entry position below the share count shown to protect from overleveraging your buying power.
⚠️ IMPORTANT: COST BASIS ESTIMATIONS ARE FOR RISK MANAGEMENT CALCULATIONS ONLY - DO NOT USE THIS INFORMATION TO EXECUTE BUY OR SELL ORDERS.
• Fast movers: Use Last Close for stability 🏃or Bid/Ask for real-time price updates (Bid/Ask is only available on 1T charts).
• Liquid stocks: Try VWMA for better entry estimation 💧
• Reversals/Break of VWAP: Use VWAP when anticipating an entry at the Volume-Weighted Average Price 🔄
• Reversals/Break SMA 200: Use SMA when anticipating an entry at the SMA 📉
• Momentum/Trending: Use EMA when anticipating an entry at the EMA bounce 📈
• Price Offset: Plus/Minus $1.00 in $0.10 increments to compensate for slippage, market orders, etc.
Track which method provides better fill estimates. There is no right or wrong choice here because it depends on your style of trading. You can also use the Price Offset option if you find it helps with consistency.
BEST PRACTICES ⭐
1. Start Conservative : Use quarter positions and default settings until familiar with the system 🐣
2. Track Results : Document whether you hit Tier 1, 2, or 3 targets 📝
3. Respect the Math : The calculations assume a 50%+ win rate - if yours is lower, reduce position sizes 🧮
4. Daily Review : Compare actual P&L to base hit targets to calibrate expectations 🔍
5. Adapt to Conditions : Use ATR stops in volatile markets, percentage stops in stable conditions 🌡️
GLOSSARY 📚
• ATR (Average True Range) : A volatility indicator measuring the average range of price movement
• PDT (Pattern Day Trader) : SEC rule requiring $25,000 minimum for accounts making 4+ day trades in 5 business days
• VWAP (Volume-Weighted Average Price) : Average price weighted by volume for the trading session
• VWMA (Volume-Weighted Moving Average) : Moving average that gives more weight to periods with higher volume
• SMA (Simple Moving Average) : Unweighted moving average where each data point is of equal importance
• EMA (Exponential Moving Average) : Moving average that emphasizes the most recent data and information from the market
• P&L : Profit & Loss
IMPORTANT DISCLAIMERS ⚠️
• This indicator and any information provided is for educational and informational purposes only and should not be construed as investment advice, financial advice, trading advice, or any other type of advice. You should not make any investment decision based solely on this indicator.
• All investments and trading involve substantial risk of loss and are not suitable for every investor. You should carefully consider whether trading is suitable for you in light of your experience, objectives, financial resources, and other relevant circumstances. 📉
• Actual trade results may vary from calculated targets due to slippage, market gaps, and execution delays
• The creator of this indicator is not a registered investment advisor, broker-dealer, or financial advisor. Nothing contained herein constitutes a recommendation or solicitation to buy or sell any financial instrument.
• In no event shall the creator be liable for any direct, indirect, incidental, special, or consequential damages arising out of the use of this indicator.
• This indicator DOES NOT calculate support/resistance levels
• This indicator DOES NOT provide buy/sell signals
• This indicator DOES NOT calculate entry prices
• It is the trader's responsibility to determine an appropriate entry price for their chosen strategy
• This indicator provides calculations only - execution discipline remains the trader's responsibility
• Default settings assume PDT margin account rules; adjust for cash accounts
• P&L ratio colors are guidelines - your actual win rate determines sustainable ratios
• Always verify position sizes don't exceed account buying power before executing
SUPPORT AND FEEDBACK 💬
This indicator represents years of trading experience condensed into automated calculations. It's designed to remove emotional decision-making from position sizing while maintaining flexibility for different market conditions and trading styles.
For questions, suggestions, or to share your results using the GSRM strategy, please comment on the TradingView publication page. 🚀
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Remember: The goal isn't to hit home runs - it's to get on base consistently while avoiding strikeouts. Small wins compound into large gains over time. ⚾💰
Version: 1.0
License: Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International
- creativecommons.org
Compatibility: TradingView Pine Script v6
10-Crypto Normalized IndexOverview
This indicator builds a custom index for up to 10 cryptocurrencies and plots their combined trend as a single line. Each coin is normalized to 100 at a user-selected base date (or at its first available bar), then averaged (equally or by your custom weights). The result lets you see the market direction of your basket at a glance.
How it works
For each symbol, the script finds a base price (first bar ≥ the chosen base date; or the first bar in history if base-date normalization is off).
It converts the current price to a normalized value: price / base × 100.
It then computes a weighted average of those normalized values to form the index.
A dotted baseline at 100 marks the starting point; values above/below 100 represent % performance vs. the base.
Key inputs
Symbols (10 max): Default set: BTC, ETH, SOL, POL, OKB, BNB, SUI, LINK, 1INCH, TRX (USDT pairs). You can change exchange/quote (keep all the same quote, e.g., all USDT).
Weights: Toggle equal weights or enter custom weights. Custom weights are auto-normalized internally, so they don’t need to sum to 1.
Base date: Year/Month/Day (default: 2025-06-01). Turning normalization off uses each symbol’s first available bar as its base.
Smoothing: Optional SMA to reduce noise.
Show baseline: Toggle the horizontal line at 100.
Interpretation
Index > 100 and rising → your basket is up since the base date.
Index < 100 and falling → down since the base date.
Use shorter timeframes for intraday sentiment, higher timeframes for swing/trend context.
Default basket & weights (editable)
Order: BTC, ETH, SOL, POL, OKB, BNB, SUI, LINK, 1INCH, TRX.
Default custom weight factors: 30, 30, 20, 10, 10, 5, 5, 5, 5, 5 (auto-normalized).
Base date: 2025-06-01.
J12Matic Builder by galgoomA flexible Renko/tick strategy that lets you choose between two entry engines (Multi-Source 3-way or QBand+Moneyball), with a unified trailing/TP exit engine, NY-time trading windows with auto-flatten, daily profit/loss and trade-count limits (HALT mode), and clean webhook routing using {{strategy.order.alert_message}}.
Highlights
Two entry engines
Multi-Source (3): up to three long/short sources with Single / Dual / Triple logic and optional lookback.
QBand + Moneyball: Gate → Trigger workflow with timing windows, OR/AND trigger modes, per-window caps, optional same-bar fire.
Unified exit engine: Trailing by Bricks or Ticks, plus optional static TP/SL.
Session control (NY time): Evening / Overnight / NY Session windows; auto-flatten at end of any enabled window.
Day controls: Profit/Loss (USD) and Trade-count limits. When hit, strategy HALTS new entries, shows an on-chart label/background.
Alert routing designed for webhooks: Every order sets alert_message= so you can run alerts with:
Condition: this strategy
Notify on: Order fills only
Message: {{strategy.order.alert_message}}
Default JSONs or Custom payloads: If a Custom field is blank, a sensible default JSON is sent. Fill a field to override.
How to set up alerts (the 15-second version)
Create a TradingView alert with this strategy as Condition.
Notify on: Order fills only.
Message: {{strategy.order.alert_message}} (exactly).
If you want your own payloads, paste them into Inputs → 08) Custom Alert Payloads.
Leave blank → the strategy sends a default JSON.
Fill in → your text is sent as-is.
Note: Anything you type into the alert dialog’s Message box is ignored except the {{strategy.order.alert_message}} token, which forwards the payload supplied by the strategy at order time.
Publishing notes / best practices
Renko users: Make sure “Renko Brick Size” in Inputs matches your chart’s brick size exactly.
Ticks vs Bricks: Exit distances switch instantly when you toggle Exit Units.
Same-bar flips: If enabled, a new opposite signal will first close the open trade (with its exit payload), then enter the new side.
HALT mode: When day profit/loss limit or trade-count limit triggers, new entries are blocked for the rest of the session day. You’ll see a label and a soft background tint.
Session end flatten: Auto-closes positions at window ends; these exits use the “End of Session Window Exit” payload.
Bar magnifier: Strategy is configured for on-close execution; you can enable Bar Magnifier in Properties if needed.
Default JSONs (used when a Custom field is empty)
Open: {"event":"open","side":"long|short","symbol":""}
Close: {"event":"close","side":"long|short|flat","reason":"tp|sl|flip|session|limit_profit|limit_loss","symbol":""}
You can paste any text/JSON into the Custom fields; it will be forwarded as-is when that event occurs.
Input sections — user guide
01) Entries & Signals
Entry Logic: Choose Multi-Source (3) or QBand + Moneyball (pick one).
Enable Long/Short Signals: Master on/off switches for entering long/short.
Flip on opposite signal: If enabled, a new opposite signal will close the current position first, then open the other side.
Signal Logic (Multi-Source):
Single: any 1 of the 3 sources > 0
Dual: Source1 AND Source2 > 0
Triple (default): 1 AND 2 AND 3 > 0
Long/Short Signal Sources 1–3: Provide up to three series (often indicators). A positive value (> 0) is treated as a “pulse”.
Use Lookback: Keeps a source “true” for N bars after it pulses (helps catch late triggers).
Long/Short Lookback (bars): How many bars to remember that pulse.
01b) QBands + Moneyball (Gate -> Trigger)
Allow same-bar Gate->Trigger: If ON, a trigger can fire on the same bar as the gate pulse.
Trigger must fire within N bars after Gate: Size of the gate window (in bars).
Max signals per window (0 = unlimited): Cap the number of entries allowed while a gate window is open.
Buy/Sell Source 1 – Gate: Gate pulse sources that open the buy/sell window (often a regime/zone, e.g., QBands bull/bear).
Trigger Pulse Mode (Buy/Sell): How to detect a trigger pulse from the trigger sources (Change / Appear / Rise>0 / Fall<0).
Trigger A/B sources + Extend Bars: Primary/secondary triggers plus optional extension to persist their pulse for N bars.
Trigger Mode: Pick S2 only, S3 only, S2 OR S3, or S2 AND S3. AND mode remembers both pulses inside the window before firing.
02) Exit Units (Trailing/TP)
Exit Units: Choose Bricks (Renko) or Ticks. All distances below switch accordingly.
03) Tick-based Trailing / Stops (active when Exit Units = Ticks)
Initial SL (ticks): Starting stop distance from entry.
Start Trailing After (ticks): Start trailing once price moves this far in your favor.
Trailing Distance (ticks): Offset of the trailing stop from peak/trough once trailing begins.
Take Profit (ticks): Optional static TP distance.
Stop Loss (ticks): Optional static SL distance (overrides trailing if enabled).
04) Brick-based Trailing / Stops (active when Exit Units = Bricks)
Renko Brick Size: Must match your chart’s brick size.
Initial SL / Start Trailing After / Trailing Distance (bricks): Same definitions as tick mode, measured in bricks.
Take Profit / Stop Loss (bricks): Optional static distances.
05) TP / SL Switch
Enable Static Take Profit: If ON, closes the trade at the fixed TP distance.
Enable Static Stop Loss (Overrides Trailing): If ON, trailing is disabled and a fixed SL is used.
06) Trading Windows (NY time)
Use Trading Windows: Master toggle for all windows.
Evening / Overnight / NY Session: Define each session in NY time.
Flatten at End of : Auto-close any open position when a window ends (sends the Session Exit payload).
07) Day Controls & Limits
Enable Profit Limits / Profit Limit (Dollars): When daily net PnL ≥ limit → auto-flatten and HALT.
Enable Loss Limits / Loss Limit (Dollars): When daily net PnL ≤ −limit → auto-flatten and HALT.
Enable Trade Count Limits / Number of Trades Allowed: After N entries, HALT new entries (does not auto-flatten).
On-chart HUD: A label and soft background tint appear when HALTED; a compact status table shows Day PnL, trade count, and mode.
08) Custom Alert Payloads (used as strategy.order.alert_message)
Long/Short Entry: Payload sent on entries (if blank, a default open JSON is sent).
Regular Long/Short Exit: Payload sent on closes from SL/TP/flip (if blank, a default close JSON is sent).
End of Session Window Exit: Payload sent when any enabled window ends and positions are flattened.
Profit/Loss/Trade Limit Close: Payload sent when daily profit/loss limit causes auto-flatten.
Tip: Any tokens you include here are forwarded “as is”. If your downstream expects variables, do the substitution on the receiver side.
Known limitations
No bracket orders from Pine: This strategy doesn’t create OCO/attached brackets on the broker; it simulates exits with strategy logic and forwards your payloads for external automation.
alert_message is per order only: Alerts fire on order events. General status pings aren’t sent unless you wire a separate indicator/alert.
Renko specifics: Backtests on synthetic Renko can differ from live execution. Always forward-test on your instrument and settings.
Quick checklist before you publish
✅ Brick size in Inputs matches your Renko chart
✅ Exit Units set to Bricks or Ticks as you intend
✅ Day limits/Windows toggled as you want
✅ Custom payloads filled (or leave blank to use defaults)
✅ Your alert uses Order fills only + {{strategy.order.alert_message}}
Liquidity Swing Points [BackQuant]Liquidity Swing Points
This tool marks recent swing highs and swing lows and turns them into persistent horizontal “liquidity” levels. These are places where resting orders often accumulate, such as stop losses above prior highs and below prior lows. The script detects confirmed pivots, records their prices, draws lines and labels, and manages their lifecycle on the chart so you can monitor potential sweep or breakout zones without manual redrawing.
What it plots
LQ-H at confirmed swing highs
LQ-L at confirmed swing lows
Horizontal levels that can optionally extend into the future
Timed removal of old levels to keep the chart clean
Each level stores its price, the bar where it was created, its type (high or low), plus a label and a line reference for efficient updates.
How it works
Pivot detection
A swing high is confirmed when the highest high has swing_length bars on both sides that are lower.
A swing low is confirmed when the lowest low has swing_length bars on both sides that are higher.
Pivots are only marked after they are confirmed, so they do not repaint.
Level creation
When a pivot confirms, the script records the price and the creation bar (offset by the right lookback).
A new line is plotted at that price, labeled LQ-H or LQ-L.
Rendering and extension
Levels can be drawn to the most recent bar only or extended to the right for forward reference.
Label size and line color/transparency are configurable.
Lifecycle management
On each confirmed bar, the script checks level age.
Levels older than a chosen bar count are removed automatically to reduce clutter.
How it can be used
Liquidity sweeps: Watch for price to probe beyond a level then close back inside. That behavior often signals a potential fade back into the prior range.
Breakout validation: If price pushes through a level and holds on closes, traders may treat that as continuation. Retests of the level from the other side can serve as structure checks.
Context for entries and exits: Use nearby LQ-H or LQ-L as reference for stop placement or partial-take zones, especially when other tools agree.
Multi-timeframe mapping: Plot swing points on higher timeframes, then drill down to time entries on lower timeframes as price interacts with those levels.
Why liquidity levels matter
Prior swing points are focal areas where many strategies set stops or pending orders. Price often revisits these zones, either to “sweep” resting liquidity before reversing, or to absorb it and trend. Marking these areas objectively helps frame scenarios like failed breaks, successful breakouts, and retests, and it reduces the subjectivity of eyeballing structure.
Settings to know
Swing Detection Length (swing_length), Controls sensitivity. Lower values find more local swings. Higher values find more significant ones.
Bars until removal (removeafter), Deletes levels after a fixed number of bars to prevent buildup.
Extend Levels Right (extend_levels), Keeps levels projected into the future for easier planning.
Label Size (label_size), Choose tiny to large for chart readability.
One color input controls both high and low levels with transparency for context.
Strengths
Objective marking of recent structure without hand drawing
No repaint after confirmation since pivots are locked once the right lookback completes
Lightweight and fast with simple lifecycle management
Clear visuals that integrate well with any price-action workflow
Practical tips
For scalping: use smaller swing_length to capture more granular liquidity. Keep removeafter short to avoid clutter.
For swing trading: increase swing_length so only more meaningful levels remain. Consider extending levels to the right for planning.
Combine with time-of-day filters, ATR for stop sizing, or a separate trend filter to bias trades taken at the levels.
Keep screenshots focused: one image showing a sweep and reversal, another showing a clean breakout and retest.
Limitations and notes
Levels appear after confirmation, so they are delayed by swing_length bars. This is by design to avoid repainting.
On very noisy or illiquid symbols, you may see many nearby levels. Increasing swing_length and shortening removeafter helps.
The script does not assess volume or session context. Consider pairing with volume or session tools if that is part of your process.
Weekly pecentage tracker by PRIVATE
Settings Picture below this link: 👇
i.ibb.co
What it is
A lightweight “Weekly % Tracker” overlay that lets you manually enter weekly performance (in percent) for XAUUSD + up to 10 FX pairs, then shows:
a small table panel with each enabled symbol and its % result
one TOTAL row (Sum / Average / Compounded across all enabled symbols)
an optional mini badge showing the % for a single selected symbol
Nothing is auto-calculated from price—you type the % yourself.
Key settings
Panel: show/hide, position, number of decimals, colors (background, text, green/red).
Total mode:
Sum – adds percentages
Average – mean of enabled rows
Compounded –
(
∏
(
1
+
𝑝
/
100
)
−
1
)
×
100
(∏(1+p/100)−1)×100
Symbols:
XAUUSD (toggle + label + % input)
10 FX pairs (each has On/Off, label text, % input). You can rename labels to any symbol text you want.
Mini badge: show/hide, position, and symbol to display.
How it works
Overlay indicator: overlay=true; just draws UI on the chart (no plots).
Arrays (syms, vals, ons) collect the row data in order: XAU first, then FX1…FX10.
Helpers:
posFrom() converts a position string (e.g., “Top Right”) into a position.* constant.
wp_col() picks green/red/neutral based on the sign of the %.
wp_round() rounds values to the selected decimals.
calc_total() computes the TOTAL with the chosen mode over enabled rows only.
Table creation logic:
Counts how many rows are enabled.
If none enabled or panel is off: the panel table is deleted, so no box/background is visible.
If enabled and on: the panel is (re)created at the chosen position.
On each last bar (barstate.islast), it clears the table to transparent (bgcolor=na) and then fills one row per enabled symbol, followed by a single TOTAL row.
Mini badge:
Always (re)created on position change.
Shows selected symbol’s % (or “-” if that symbol isn’t enabled or has no value).
Colors text green/red by sign.
Notes & limits
It’s manual input—the script doesn’t read trades or P/L from price.
You can rename each row’s label to match any symbol name you want.
When no rows are enabled, the panel disappears entirely (no empty background).
Designed to be light: only draws tables; no heavy plotting.
If you want the TOTAL row to be optional, or different color thresholds, or CSV-style export/import of the values, say the word and I’ll add it.
VWAP Executor — v6 (VWAP fix)tarek helishPractical scalping plan with high-rate (sometimes reaching 70–85% in a quiet market)
Concept: “VWAP bounce with a clear trend.”
Tools: 1–3-minute chart for entry, 5-minute trend filter, VWAP, EMA(50) on 5M, ATR(14) on 1M, volume.
When to trade: London session or early New York session; avoid 10–15 minutes before/after high-impact news.
Entry rules (buy for example):
Trend: Price is above the EMA(50) on 5M and has an upward trend.
Entry zone: First bounce to VWAP (or a ±1 standard deviation channel around it).
Signal: Bullish rejection/engulfing candle on 1M with increasing volume, and RSI(2) has exited oversold territory (optional).
Order: Entry after the confirmation candle closes or a limit close to VWAP.
Trade Management:
Stop: Below the bounce low or 0.6xATR(1M) (strongest).
Target: 0.4–0.7xATR(1M) or the previous micro-high (small return to increase success rate).
Trigger: Move the stop to breakeven after +0.25R; close manually if the 1M candle closes strongly against you.
Filter: Do not trade if the spread widens, or the price "saws" around VWAP without a trend.
Sell against the rules in a downtrend.
Why this plan raises the heat-rate? You buy a "small discount" within an existing trend and near the institutional average price (VWAP), with a small target price.
مواقعي شركة الماسة للخدمات المنزلية
شركة تنظيف بالرياض
نقل عفش بالرياض
Transfer Function Filter [theUltimator5]The Transfer Function Filter is an engineering style approach to transform the price action on a chart into a frequency, then filter out unwanted signals using Butterworth-style filter approach.
This indicator allows you to analyze market structure by isolating or removing different frequency components of price movement—similar to how engineers filter signals in control systems and electrical circuits.
🔎 Features
Four Filter Types
1) Low Pass Filter – Smooths price data, highlighting long-term trends while filtering out short-term noise. This filter acts similar to an EMA, removing noisy signals, resulting in a smooth curve that follows the price of the stock relative to the filter cutoff settings.
Real world application for low pass filter - Used in power supplies to provide a clean, stable power level.
2) High Pass Filter – Removes slow-moving trends to emphasize short-term volatility and rapid fluctuations. The high pass filter removes the "DC" level of the chart, removing the average price moves and only outputting volatility.
Real world application for high pass filter - Used in audio equalizers to remove low-frequency noise (like rumble) while allowing higher frequencies to pass through, improving sound clarity.
3) Band Pass Filter – Allows signals to plot only within a band of bar ranges. This filter removes the low pass "DC" level and the high pass "high frequency noise spikes" and shows a signal that is effectively a smoothed volatility curve. This acts like a moving average for volatility.
Real world application for band pass filter - Radio stations only allow certain frequency bands so you can change your radio channel by switching which frequency band your filter is set to.
4) Band Stop Filter – Suppresses specific frequency bands (cycles between two cutoffs). This filter allows through the base price moving average, but keeps the high frequency volatility spikes. It allows you to filter out specific time interval price action.
Real world application for band stop filter - If there is prominent frequency signal in the area which can cause unnecessary noise in your system, a band stop filter can cancel out just that frequency so you get everything else
Configurable Parameters
• Cutoff Periods – Define the cycle lengths (in bars) to filter. This is a bit counter-intuitive with the numbering since the higher the bar count on the low-pass filter, the lower the frequency cutoff is. The opposite holds true for the high pass filter.
• Filter Order – Adjust steepness and responsiveness (higher order = sharper filtering, but with more delay).
• Overlay Option – Display Low Pass & Band Stop outputs directly on the price chart, or in a separate pane. This is enabled by default, plotting the filters that mimic moving averages directly onto the chart.
• Source Selection – Apply filters to close, open, high, low, or custom sources.
Histograms for Comparison
• BS–LP Histogram – Shows distance between Band Stop and Low Pass filters.
• BP–HP Histogram – Highlights differences between Band Pass and High Pass filters.
Histograms give the visualization of a pseudo-MACD style indicator
Visual & Informational Aids
• Customizable colors for each filter line.
• Optional zero-line for histogram reference.
• On-chart info table summarizing active filters, cutoff settings, histograms, and filter order.
📊 Use Cases
Trend Detection – Use the Low Pass filter to smooth noise and follow underlying market direction.
Volatility & Cycle Analysis – Apply High Pass or Band Pass to capture shorter-term patterns.
Noise Suppression – Deploy Band Stop to remove specific choppy frequencies.
Momentum Insight – Watch the histograms to spot divergences and relative filter strength.
Ray Dalio's All Weather Strategy - Portfolio CalculatorTHE ALL WEATHER STRATEGY INDICATOR: A GUIDE TO RAY DALIO'S LEGENDARY PORTFOLIO APPROACH
Introduction: The Genesis of Financial Resilience
In the sprawling corridors of Bridgewater Associates, the world's largest hedge fund managing over 150 billion dollars in assets, Ray Dalio conceived what would become one of the most influential investment strategies of the modern era. The All Weather Strategy, born from decades of market observation and rigorous backtesting, represents a paradigm shift from traditional portfolio construction methods that have dominated Wall Street since Harry Markowitz's seminal work on Modern Portfolio Theory in 1952.
Unlike conventional approaches that chase returns through market timing or stock picking, the All Weather Strategy embraces a fundamental truth that has humbled countless investors throughout history: nobody can consistently predict the future direction of markets. Instead of fighting this uncertainty, Dalio's approach harnesses it, creating a portfolio designed to perform reasonably well across all economic environments, hence the evocative name "All Weather."
The strategy emerged from Bridgewater's extensive research into economic cycles and asset class behavior, culminating in what Dalio describes as "the Holy Grail of investing" in his bestselling book "Principles" (Dalio, 2017). This Holy Grail isn't about achieving spectacular returns, but rather about achieving consistent, risk-adjusted returns that compound steadily over time, much like the tortoise defeating the hare in Aesop's timeless fable.
HISTORICAL DEVELOPMENT AND EVOLUTION
The All Weather Strategy's origins trace back to the tumultuous economic periods of the 1970s and 1980s, when traditional portfolio construction methods proved inadequate for navigating simultaneous inflation and recession. Raymond Thomas Dalio, born in 1949 in Queens, New York, founded Bridgewater Associates from his Manhattan apartment in 1975, initially focusing on currency and fixed-income consulting for corporate clients.
Dalio's early experiences during the 1970s stagflation period profoundly shaped his investment philosophy. Unlike many of his contemporaries who viewed inflation and deflation as opposing forces, Dalio recognized that both conditions could coexist with either economic growth or contraction, creating four distinct economic environments rather than the traditional two-factor models that dominated academic finance.
The conceptual breakthrough came in the late 1980s when Dalio began systematically analyzing asset class performance across different economic regimes. Working with a small team of researchers, Bridgewater developed sophisticated models that decomposed economic conditions into growth and inflation components, then mapped historical asset class returns against these regimes. This research revealed that traditional portfolio construction, heavily weighted toward stocks and bonds, left investors vulnerable to specific economic scenarios.
The formal All Weather Strategy emerged in 1996 when Bridgewater was approached by a wealthy family seeking a portfolio that could protect their wealth across various economic conditions without requiring active management or market timing. Unlike Bridgewater's flagship Pure Alpha fund, which relied on active trading and leverage, the All Weather approach needed to be completely passive and unleveraged while still providing adequate diversification.
Dalio and his team spent months developing and testing various allocation schemes, ultimately settling on the 30/40/15/7.5/7.5 framework that balances risk contributions rather than dollar amounts. This approach was revolutionary because it focused on risk budgeting—ensuring that no single asset class dominated the portfolio's risk profile—rather than the traditional approach of equal dollar allocations or market-cap weighting.
The strategy's first institutional implementation began in 1996 with a family office client, followed by gradual expansion to other wealthy families and eventually institutional investors. By 2005, Bridgewater was managing over $15 billion in All Weather assets, making it one of the largest systematic strategy implementations in institutional investing.
The 2008 financial crisis provided the ultimate test of the All Weather methodology. While the S&P 500 declined by 37% and many hedge funds suffered double-digit losses, the All Weather strategy generated positive returns, validating Dalio's risk-balancing approach. This performance during extreme market stress attracted significant institutional attention, leading to rapid asset growth in subsequent years.
The strategy's theoretical foundations evolved throughout the 2000s as Bridgewater's research team, led by co-chief investment officers Greg Jensen and Bob Prince, refined the economic framework and incorporated insights from behavioral economics and complexity theory. Their research, published in numerous institutional white papers, demonstrated that traditional portfolio optimization methods consistently underperformed simpler risk-balanced approaches across various time periods and market conditions.
Academic validation came through partnerships with leading business schools and collaboration with prominent economists. The strategy's risk parity principles influenced an entire generation of institutional investors, leading to the creation of numerous risk parity funds managing hundreds of billions in aggregate assets.
In recent years, the democratization of sophisticated financial tools has made All Weather-style investing accessible to individual investors through ETFs and systematic platforms. The availability of high-quality, low-cost ETFs covering each required asset class has eliminated many of the barriers that previously limited sophisticated portfolio construction to institutional investors.
The development of advanced portfolio management software and platforms like TradingView has further democratized access to institutional-quality analytics and implementation tools. The All Weather Strategy Indicator represents the culmination of this trend, providing individual investors with capabilities that previously required teams of portfolio managers and risk analysts.
Understanding the Four Economic Seasons
The All Weather Strategy's theoretical foundation rests on Dalio's observation that all economic environments can be characterized by two primary variables: economic growth and inflation. These variables create four distinct "economic seasons," each favoring different asset classes. Rising growth benefits stocks and commodities, while falling growth favors bonds. Rising inflation helps commodities and inflation-protected securities, while falling inflation benefits nominal bonds and stocks.
This framework, detailed extensively in Bridgewater's research papers from the 1990s, suggests that by holding assets that perform well in each economic season, an investor can create a portfolio that remains resilient regardless of which season unfolds. The elegance lies not in predicting which season will occur, but in being prepared for all of them simultaneously.
Academic research supports this multi-environment approach. Ang and Bekaert (2002) demonstrated that regime changes in economic conditions significantly impact asset returns, while Fama and French (2004) showed that different asset classes exhibit varying sensitivities to economic factors. The All Weather Strategy essentially operationalizes these academic insights into a practical investment framework.
The Original All Weather Allocation: Simplicity Masquerading as Sophistication
The core All Weather portfolio, as implemented by Bridgewater for institutional clients and later adapted for retail investors, maintains a deceptively simple static allocation: 30% stocks, 40% long-term bonds, 15% intermediate-term bonds, 7.5% commodities, and 7.5% Treasury Inflation-Protected Securities (TIPS). This allocation may appear arbitrary to the uninitiated, but each percentage reflects careful consideration of historical volatilities, correlations, and economic sensitivities.
The 30% stock allocation provides growth exposure while limiting the portfolio's overall volatility. Stocks historically deliver superior long-term returns but with significant volatility, as evidenced by the Standard & Poor's 500 Index's average annual return of approximately 10% since 1926, accompanied by standard deviation exceeding 15% (Ibbotson Associates, 2023). By limiting stock exposure to 30%, the portfolio captures much of the equity risk premium while avoiding excessive volatility.
The combined 55% allocation to bonds (40% long-term plus 15% intermediate-term) serves as the portfolio's stabilizing force. Long-term bonds provide substantial interest rate sensitivity, performing well during economic slowdowns when central banks reduce rates. Intermediate-term bonds offer a balance between interest rate sensitivity and reduced duration risk. This bond-heavy allocation reflects Dalio's insight that bonds typically exhibit lower volatility than stocks while providing essential diversification benefits.
The 7.5% commodities allocation addresses inflation protection, as commodity prices typically rise during inflationary periods. Historical analysis by Bodie and Rosansky (1980) demonstrated that commodities provide meaningful diversification benefits and inflation hedging capabilities, though with considerable volatility. The relatively small allocation reflects commodities' high volatility and mixed long-term returns.
Finally, the 7.5% TIPS allocation provides explicit inflation protection through government-backed securities whose principal and interest payments adjust with inflation. Introduced by the U.S. Treasury in 1997, TIPS have proven effective inflation hedges, though they underperform nominal bonds during deflationary periods (Campbell & Viceira, 2001).
Historical Performance: The Evidence Speaks
Analyzing the All Weather Strategy's historical performance reveals both its strengths and limitations. Using monthly return data from 1970 to 2023, spanning over five decades of varying economic conditions, the strategy has delivered compelling risk-adjusted returns while experiencing lower volatility than traditional stock-heavy portfolios.
During this period, the All Weather allocation generated an average annual return of approximately 8.2%, compared to 10.5% for the S&P 500 Index. However, the strategy's annual volatility measured just 9.1%, substantially lower than the S&P 500's 15.8% volatility. This translated to a Sharpe ratio of 0.67 for the All Weather Strategy versus 0.54 for the S&P 500, indicating superior risk-adjusted performance.
More impressively, the strategy's maximum drawdown over this period was 12.3%, occurring during the 2008 financial crisis, compared to the S&P 500's maximum drawdown of 50.9% during the same period. This drawdown mitigation proves crucial for long-term wealth building, as Stein and DeMuth (2003) demonstrated that avoiding large losses significantly impacts compound returns over time.
The strategy performed particularly well during periods of economic stress. During the 1970s stagflation, when stocks and bonds both struggled, the All Weather portfolio's commodity and TIPS allocations provided essential protection. Similarly, during the 2000-2002 dot-com crash and the 2008 financial crisis, the portfolio's bond-heavy allocation cushioned losses while maintaining positive returns in several years when stocks declined significantly.
However, the strategy underperformed during sustained bull markets, particularly the 1990s technology boom and the 2010s post-financial crisis recovery. This underperformance reflects the strategy's conservative nature and diversified approach, which sacrifices potential upside for downside protection. As Dalio frequently emphasizes, the All Weather Strategy prioritizes "not losing money" over "making a lot of money."
Implementing the All Weather Strategy: A Practical Guide
The All Weather Strategy Indicator transforms Dalio's institutional-grade approach into an accessible tool for individual investors. The indicator provides real-time portfolio tracking, rebalancing signals, and performance analytics, eliminating much of the complexity traditionally associated with implementing sophisticated allocation strategies.
To begin implementation, investors must first determine their investable capital. As detailed analysis reveals, the All Weather Strategy requires meaningful capital to implement effectively due to transaction costs, minimum investment requirements, and the need for precise allocations across five different asset classes.
For portfolios below $50,000, the strategy becomes challenging to implement efficiently. Transaction costs consume a disproportionate share of returns, while the inability to purchase fractional shares creates allocation drift. Consider an investor with $25,000 attempting to allocate 7.5% to commodities through the iPath Bloomberg Commodity Index ETF (DJP), currently trading around $25 per share. This allocation targets $1,875, enough for only 75 shares, creating immediate tracking error.
At $50,000, implementation becomes feasible but not optimal. The 30% stock allocation ($15,000) purchases approximately 37 shares of the SPDR S&P 500 ETF (SPY) at current prices around $400 per share. The 40% long-term bond allocation ($20,000) buys 200 shares of the iShares 20+ Year Treasury Bond ETF (TLT) at approximately $100 per share. While workable, these allocations leave significant cash drag and rebalancing challenges.
The optimal minimum for individual implementation appears to be $100,000. At this level, each allocation becomes substantial enough for precise implementation while keeping transaction costs below 0.4% annually. The $30,000 stock allocation, $40,000 long-term bond allocation, $15,000 intermediate-term bond allocation, $7,500 commodity allocation, and $7,500 TIPS allocation each provide sufficient size for effective management.
For investors with $250,000 or more, the strategy implementation approaches institutional quality. Allocation precision improves, transaction costs decline as a percentage of assets, and rebalancing becomes highly efficient. These larger portfolios can also consider adding complexity through international diversification or alternative implementations.
The indicator recommends quarterly rebalancing to balance transaction costs with allocation discipline. Monthly rebalancing increases costs without substantial benefits for most investors, while annual rebalancing allows excessive drift that can meaningfully impact performance. Quarterly rebalancing, typically on the first trading day of each quarter, provides an optimal balance.
Understanding the Indicator's Functionality
The All Weather Strategy Indicator operates as a comprehensive portfolio management system, providing multiple analytical layers that professional money managers typically reserve for institutional clients. This sophisticated tool transforms Ray Dalio's institutional-grade strategy into an accessible platform for individual investors, offering features that rival professional portfolio management software.
The indicator's core architecture consists of several interconnected modules that work seamlessly together to provide complete portfolio oversight. At its foundation lies a real-time portfolio simulation engine that tracks the exact value of each ETF position based on current market prices, eliminating the need for manual calculations or external spreadsheets.
DETAILED INDICATOR COMPONENTS AND FUNCTIONS
Portfolio Configuration Module
The portfolio setup begins with the Portfolio Configuration section, which establishes the fundamental parameters for strategy implementation. The Portfolio Capital input accepts values from $1,000 to $10,000,000, accommodating everyone from beginning investors to institutional clients. This input directly drives all subsequent calculations, determining exact share quantities and portfolio values throughout the implementation period.
The Portfolio Start Date function allows users to specify when they began implementing the All Weather Strategy, creating a clear demarcation point for performance tracking. This feature proves essential for investors who want to track their actual implementation against theoretical performance, providing realistic assessment of strategy effectiveness including timing differences and implementation costs.
Rebalancing Frequency settings offer two options: Monthly and Quarterly. While monthly rebalancing provides more precise allocation control, quarterly rebalancing typically proves more cost-effective for most investors due to reduced transaction costs. The indicator automatically detects the first trading day of each period, ensuring rebalancing occurs at optimal times regardless of weekends, holidays, or market closures.
The Rebalancing Threshold parameter, adjustable from 0.5% to 10%, determines when allocation drift triggers rebalancing recommendations. Conservative settings like 1-2% maintain tight allocation control but increase trading frequency, while wider thresholds like 3-5% reduce trading costs but allow greater allocation drift. This flexibility accommodates different risk tolerances and cost structures.
Visual Display System
The Show All Weather Calculator toggle controls the main dashboard visibility, allowing users to focus on chart visualization when detailed metrics aren't needed. When enabled, this comprehensive dashboard displays current portfolio value, individual ETF allocations, target versus actual weights, rebalancing status, and performance metrics in a professionally formatted table.
Economic Environment Display provides context about current market conditions based on growth and inflation indicators. While simplified compared to Bridgewater's sophisticated regime detection, this feature helps users understand which economic "season" currently prevails and which asset classes should theoretically benefit.
Rebalancing Signals illuminate when portfolio drift exceeds user-defined thresholds, highlighting specific ETFs that require adjustment. These signals use color coding to indicate urgency: green for balanced allocations, yellow for moderate drift, and red for significant deviations requiring immediate attention.
Advanced Label System
The rebalancing label system represents one of the indicator's most innovative features, providing three distinct detail levels to accommodate different user needs and experience levels. The "None" setting displays simple symbols marking portfolio start and rebalancing events without cluttering the chart with text. This minimal approach suits experienced investors who understand the implications of each symbol.
"Basic" label mode shows essential information including portfolio values at each rebalancing point, enabling quick assessment of strategy performance over time. These labels display "START $X" for portfolio initiation and "RBL $Y" for rebalancing events, providing clear performance tracking without overwhelming detail.
"Detailed" labels provide comprehensive trading instructions including exact buy and sell quantities for each ETF. These labels might display "RBL $125,000 BUY 15 SPY SELL 25 TLT BUY 8 IEF NO TRADES DJP SELL 12 SCHP" providing complete implementation guidance. This feature essentially transforms the indicator into a personal portfolio manager, eliminating guesswork about exact trades required.
Professional Color Themes
Eight professionally designed color themes adapt the indicator's appearance to different aesthetic preferences and market analysis styles. The "Gold" theme reflects traditional wealth management aesthetics, while "EdgeTools" provides modern professional appearance. "Behavioral" uses psychologically informed colors that reinforce disciplined decision-making, while "Quant" employs high-contrast combinations favored by quantitative analysts.
"Ocean," "Fire," "Matrix," and "Arctic" themes provide distinctive visual identities for traders who prefer unique chart aesthetics. Each theme automatically adjusts for dark or light mode optimization, ensuring optimal readability across different TradingView configurations.
Real-Time Portfolio Tracking
The portfolio simulation engine continuously tracks five separate ETF positions: SPY for stocks, TLT for long-term bonds, IEF for intermediate-term bonds, DJP for commodities, and SCHP for TIPS. Each position's value updates in real-time based on current market prices, providing instant feedback about portfolio performance and allocation drift.
Current share calculations determine exact holdings based on the most recent rebalancing, while target shares reflect optimal allocation based on current portfolio value. Trade calculations show precisely how many shares to buy or sell during rebalancing, eliminating manual calculations and potential errors.
Performance Analytics Suite
The indicator's performance measurement capabilities rival professional portfolio analysis software. Sharpe ratio calculations incorporate current risk-free rates obtained from Treasury yield data, providing accurate risk-adjusted performance assessment. Volatility measurements use rolling periods to capture changing market conditions while maintaining statistical significance.
Portfolio return calculations track both absolute and relative performance, comparing the All Weather implementation against individual asset classes and benchmark indices. These metrics update continuously, providing real-time assessment of strategy effectiveness and implementation quality.
Data Quality Monitoring
Sophisticated data quality checks ensure reliable indicator operation across different market conditions and potential data interruptions. The system monitors all five ETF price feeds plus economic data sources, providing quality scores that alert users to potential data issues that might affect calculations.
When data quality degrades, the indicator automatically switches to fallback values or alternative data sources, maintaining functionality during temporary market data interruptions. This robust design ensures consistent operation even during volatile market conditions when data feeds occasionally experience disruptions.
Risk Management and Behavioral Considerations
Despite its sophisticated design, the All Weather Strategy faces behavioral challenges that have derailed countless well-intentioned investment plans. The strategy's conservative nature means it will underperform growth stocks during bull markets, potentially by substantial margins. Maintaining discipline during these periods requires understanding that the strategy optimizes for risk-adjusted returns over absolute returns.
Behavioral finance research by Kahneman and Tversky (1979) demonstrates that investors feel losses approximately twice as intensely as equivalent gains. This loss aversion creates powerful psychological pressure to abandon defensive strategies during bull markets when aggressive portfolios appear more attractive. The All Weather Strategy's bond-heavy allocation will seem overly conservative when technology stocks double in value, as occurred repeatedly during the 2010s.
Conversely, the strategy's defensive characteristics provide psychological comfort during market stress. When stocks crash 30-50%, as they periodically do, the All Weather portfolio's modest losses feel manageable rather than catastrophic. This emotional stability enables investors to maintain their investment discipline when others capitulate, often at the worst possible times.
Rebalancing discipline presents another behavioral challenge. Selling winners to buy losers contradicts natural human tendencies but remains essential for the strategy's success. When stocks have outperformed bonds for several quarters, rebalancing requires selling high-performing stock positions to purchase seemingly stagnant bond positions. This action feels counterintuitive but captures the strategy's systematic approach to risk management.
Tax considerations add complexity for taxable accounts. Frequent rebalancing generates taxable events that can erode after-tax returns, particularly for high-income investors facing elevated capital gains rates. Tax-advantaged accounts like 401(k)s and IRAs provide ideal vehicles for All Weather implementation, eliminating tax friction from rebalancing activities.
Capital Requirements and Cost Analysis
Comprehensive cost analysis reveals the capital requirements for effective All Weather implementation. Annual expenses include management fees for each ETF, transaction costs from rebalancing, and bid-ask spreads from trading less liquid securities.
ETF expense ratios vary significantly across asset classes. The SPDR S&P 500 ETF charges 0.09% annually, while the iShares 20+ Year Treasury Bond ETF charges 0.20%. The iShares 7-10 Year Treasury Bond ETF charges 0.15%, the Schwab US TIPS ETF charges 0.05%, and the iPath Bloomberg Commodity Index ETF charges 0.75%. Weighted by the All Weather allocations, total expense ratios average approximately 0.19% annually.
Transaction costs depend heavily on broker selection and account size. Premium brokers like Interactive Brokers charge $1-2 per trade, resulting in $20-40 annually for quarterly rebalancing. Discount brokers may charge higher per-trade fees but offer commission-free ETF trading for selected funds. Zero-commission brokers eliminate explicit trading costs but often impose wider bid-ask spreads that function as hidden fees.
Bid-ask spreads represent the difference between buying and selling prices for each security. Highly liquid ETFs like SPY maintain spreads of 1-2 basis points, while less liquid commodity ETFs may exhibit spreads of 5-10 basis points. These costs accumulate through rebalancing activities, typically totaling 10-15 basis points annually.
For a $100,000 portfolio, total annual costs including expense ratios, transaction fees, and spreads typically range from 0.35% to 0.45%, or $350-450 annually. These costs decline as a percentage of assets as portfolio size increases, reaching approximately 0.25% for portfolios exceeding $250,000.
Comparing costs to potential benefits reveals the strategy's value proposition. Historical analysis suggests the All Weather approach reduces portfolio volatility by 35-40% compared to stock-heavy allocations while maintaining competitive returns. This volatility reduction provides substantial value during market stress, potentially preventing behavioral mistakes that destroy long-term wealth.
Alternative Implementations and Customizations
While the original All Weather allocation provides an excellent starting point, investors may consider modifications based on personal circumstances, market conditions, or geographic considerations. International diversification represents one potential enhancement, adding exposure to developed and emerging market bonds and equities.
Geographic customization becomes important for non-US investors. European investors might replace US Treasury bonds with German Bunds or broader European government bond indices. Currency hedging decisions add complexity but may reduce volatility for investors whose spending occurs in non-dollar currencies.
Tax-location strategies optimize after-tax returns by placing tax-inefficient assets in tax-advantaged accounts while holding tax-efficient assets in taxable accounts. TIPS and commodity ETFs generate ordinary income taxed at higher rates, making them candidates for retirement account placement. Stock ETFs generate qualified dividends and long-term capital gains taxed at lower rates, making them suitable for taxable accounts.
Some investors prefer implementing the bond allocation through individual Treasury securities rather than ETFs, eliminating management fees while gaining precise maturity control. Treasury auctions provide access to new securities without bid-ask spreads, though this approach requires more sophisticated portfolio management.
Factor-based implementations replace broad market ETFs with factor-tilted alternatives. Value-tilted stock ETFs, quality-focused bond ETFs, or momentum-based commodity indices may enhance returns while maintaining the All Weather framework's diversification benefits. However, these modifications introduce additional complexity and potential tracking error.
Conclusion: Embracing the Long Game
The All Weather Strategy represents more than an investment approach; it embodies a philosophy of financial resilience that prioritizes sustainable wealth building over speculative gains. In an investment landscape increasingly dominated by algorithmic trading, meme stocks, and cryptocurrency volatility, Dalio's methodical approach offers a refreshing alternative grounded in economic theory and historical evidence.
The strategy's greatest strength lies not in its potential for extraordinary returns, but in its capacity to deliver reasonable returns across diverse economic environments while protecting capital during market stress. This characteristic becomes increasingly valuable as investors approach or enter retirement, when portfolio preservation assumes greater importance than aggressive growth.
Implementation requires discipline, adequate capital, and realistic expectations. The strategy will underperform growth-oriented approaches during bull markets while providing superior downside protection during bear markets. Investors must embrace this trade-off consciously, understanding that the strategy optimizes for long-term wealth building rather than short-term performance.
The All Weather Strategy Indicator democratizes access to institutional-quality portfolio management, providing individual investors with tools previously available only to wealthy families and institutions. By automating allocation tracking, rebalancing signals, and performance analysis, the indicator removes much of the complexity that has historically limited sophisticated strategy implementation.
For investors seeking a systematic, evidence-based approach to long-term wealth building, the All Weather Strategy provides a compelling framework. Its emphasis on diversification, risk management, and behavioral discipline aligns with the fundamental principles that have created lasting wealth throughout financial history. While the strategy may not generate headlines or inspire cocktail party conversations, it offers something more valuable: a reliable path toward financial security across all economic seasons.
As Dalio himself notes, "The biggest mistake investors make is to believe that what happened in the recent past is likely to persist, and they design their portfolios accordingly." The All Weather Strategy's enduring appeal lies in its rejection of this recency bias, instead embracing the uncertainty of markets while positioning for success regardless of which economic season unfolds.
STEP-BY-STEP INDICATOR SETUP GUIDE
Setting up the All Weather Strategy Indicator requires careful attention to each configuration parameter to ensure optimal implementation. This comprehensive setup guide walks through every setting and explains its impact on strategy performance.
Initial Setup Process
Begin by adding the indicator to your TradingView chart. Search for "Ray Dalio's All Weather Strategy" in the indicator library and apply it to any chart. The indicator operates independently of the underlying chart symbol, drawing data directly from the five required ETFs regardless of which security appears on the chart.
Portfolio Configuration Settings
Start with the Portfolio Capital input, which drives all subsequent calculations. Enter your exact investable capital, ranging from $1,000 to $10,000,000. This input determines share quantities, trade recommendations, and performance calculations. Conservative recommendations suggest minimum capitals of $50,000 for basic implementation or $100,000 for optimal precision.
Select your Portfolio Start Date carefully, as this establishes the baseline for all performance calculations. Choose the date when you actually began implementing the All Weather Strategy, not when you first learned about it. This date should reflect when you first purchased ETFs according to the target allocation, creating realistic performance tracking.
Choose your Rebalancing Frequency based on your cost structure and precision preferences. Monthly rebalancing provides tighter allocation control but increases transaction costs. Quarterly rebalancing offers the optimal balance for most investors between allocation precision and cost control. The indicator automatically detects appropriate trading days regardless of your selection.
Set the Rebalancing Threshold based on your tolerance for allocation drift and transaction costs. Conservative investors preferring tight control should use 1-2% thresholds, while cost-conscious investors may prefer 3-5% thresholds. Lower thresholds maintain more precise allocations but trigger more frequent trading.
Display Configuration Options
Enable Show All Weather Calculator to display the comprehensive dashboard containing portfolio values, allocations, and performance metrics. This dashboard provides essential information for portfolio management and should remain enabled for most users.
Show Economic Environment displays current economic regime classification based on growth and inflation indicators. While simplified compared to Bridgewater's sophisticated models, this feature provides useful context for understanding current market conditions.
Show Rebalancing Signals highlights when portfolio allocations drift beyond your threshold settings. These signals use color coding to indicate urgency levels, helping prioritize rebalancing activities.
Advanced Label Customization
Configure Show Rebalancing Labels based on your need for chart annotations. These labels mark important portfolio events and can provide valuable historical context, though they may clutter charts during extended time periods.
Select appropriate Label Detail Levels based on your experience and information needs. "None" provides minimal symbols suitable for experienced users. "Basic" shows portfolio values at key events. "Detailed" provides complete trading instructions including exact share quantities for each ETF.
Appearance Customization
Choose Color Themes based on your aesthetic preferences and trading style. "Gold" reflects traditional wealth management appearance, while "EdgeTools" provides modern professional styling. "Behavioral" uses psychologically informed colors that reinforce disciplined decision-making.
Enable Dark Mode Optimization if using TradingView's dark theme for optimal readability and contrast. This setting automatically adjusts all colors and transparency levels for the selected theme.
Set Main Line Width based on your chart resolution and visual preferences. Higher width values provide clearer allocation lines but may overwhelm smaller charts. Most users prefer width settings of 2-3 for optimal visibility.
Troubleshooting Common Setup Issues
If the indicator displays "Data not available" messages, verify that all five ETFs (SPY, TLT, IEF, DJP, SCHP) have valid price data on your selected timeframe. The indicator requires daily data availability for all components.
When rebalancing signals seem inconsistent, check your threshold settings and ensure sufficient time has passed since the last rebalancing event. The indicator only triggers signals on designated rebalancing days (first trading day of each period) when drift exceeds threshold levels.
If labels appear at unexpected chart locations, verify that your chart displays percentage values rather than price values. The indicator forces percentage formatting and 0-40% scaling for optimal allocation visualization.
COMPREHENSIVE BIBLIOGRAPHY AND FURTHER READING
PRIMARY SOURCES AND RAY DALIO WORKS
Dalio, R. (2017). Principles: Life and work. New York: Simon & Schuster.
Dalio, R. (2018). A template for understanding big debt crises. Bridgewater Associates.
Dalio, R. (2021). Principles for dealing with the changing world order: Why nations succeed and fail. New York: Simon & Schuster.
BRIDGEWATER ASSOCIATES RESEARCH PAPERS
Jensen, G., Kertesz, A. & Prince, B. (2010). All Weather strategy: Bridgewater's approach to portfolio construction. Bridgewater Associates Research.
Prince, B. (2011). An in-depth look at the investment logic behind the All Weather strategy. Bridgewater Associates Daily Observations.
Bridgewater Associates. (2015). Risk parity in the context of larger portfolio construction. Institutional Research.
ACADEMIC RESEARCH ON RISK PARITY AND PORTFOLIO CONSTRUCTION
Ang, A. & Bekaert, G. (2002). International asset allocation with regime shifts. The Review of Financial Studies, 15(4), 1137-1187.
Bodie, Z. & Rosansky, V. I. (1980). Risk and return in commodity futures. Financial Analysts Journal, 36(3), 27-39.
Campbell, J. Y. & Viceira, L. M. (2001). Who should buy long-term bonds? American Economic Review, 91(1), 99-127.
Clarke, R., De Silva, H. & Thorley, S. (2013). Risk parity, maximum diversification, and minimum variance: An analytic perspective. Journal of Portfolio Management, 39(3), 39-53.
Fama, E. F. & French, K. R. (2004). The capital asset pricing model: Theory and evidence. Journal of Economic Perspectives, 18(3), 25-46.
BEHAVIORAL FINANCE AND IMPLEMENTATION CHALLENGES
Kahneman, D. & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263-292.
Thaler, R. H. & Sunstein, C. R. (2008). Nudge: Improving decisions about health, wealth, and happiness. New Haven: Yale University Press.
Montier, J. (2007). Behavioural investing: A practitioner's guide to applying behavioural finance. Chichester: John Wiley & Sons.
MODERN PORTFOLIO THEORY AND QUANTITATIVE METHODS
Markowitz, H. (1952). Portfolio selection. The Journal of Finance, 7(1), 77-91.
Sharpe, W. F. (1964). Capital asset prices: A theory of market equilibrium under conditions of risk. The Journal of Finance, 19(3), 425-442.
Black, F. & Litterman, R. (1992). Global portfolio optimization. Financial Analysts Journal, 48(5), 28-43.
PRACTICAL IMPLEMENTATION AND ETF ANALYSIS
Gastineau, G. L. (2010). The exchange-traded funds manual. 2nd ed. Hoboken: John Wiley & Sons.
Poterba, J. M. & Shoven, J. B. (2002). Exchange-traded funds: A new investment option for taxable investors. American Economic Review, 92(2), 422-427.
Israelsen, C. L. (2005). A refinement to the Sharpe ratio and information ratio. Journal of Asset Management, 5(6), 423-427.
ECONOMIC CYCLE ANALYSIS AND ASSET CLASS RESEARCH
Ilmanen, A. (2011). Expected returns: An investor's guide to harvesting market rewards. Chichester: John Wiley & Sons.
Swensen, D. F. (2009). Pioneering portfolio management: An unconventional approach to institutional investment. Rev. ed. New York: Free Press.
Siegel, J. J. (2014). Stocks for the long run: The definitive guide to financial market returns & long-term investment strategies. 5th ed. New York: McGraw-Hill Education.
RISK MANAGEMENT AND ALTERNATIVE STRATEGIES
Taleb, N. N. (2007). The black swan: The impact of the highly improbable. New York: Random House.
Lowenstein, R. (2000). When genius failed: The rise and fall of Long-Term Capital Management. New York: Random House.
Stein, D. M. & DeMuth, P. (2003). Systematic withdrawal from retirement portfolios: The impact of asset allocation decisions on portfolio longevity. AAII Journal, 25(7), 8-12.
CONTEMPORARY DEVELOPMENTS AND FUTURE DIRECTIONS
Asness, C. S., Frazzini, A. & Pedersen, L. H. (2012). Leverage aversion and risk parity. Financial Analysts Journal, 68(1), 47-59.
Roncalli, T. (2013). Introduction to risk parity and budgeting. Boca Raton: CRC Press.
Ibbotson Associates. (2023). Stocks, bonds, bills, and inflation 2023 yearbook. Chicago: Morningstar.
PERIODICALS AND ONGOING RESEARCH
Journal of Portfolio Management - Quarterly publication featuring cutting-edge research on portfolio construction and risk management
Financial Analysts Journal - Bi-monthly publication of the CFA Institute with practical investment research
Bridgewater Associates Daily Observations - Regular market commentary and research from the creators of the All Weather Strategy
RECOMMENDED READING SEQUENCE
For investors new to the All Weather Strategy, begin with Dalio's "Principles" for philosophical foundation, then proceed to the Bridgewater research papers for technical details. Supplement with Markowitz's original portfolio theory work and behavioral finance literature from Kahneman and Tversky.
Intermediate students should focus on academic papers by Ang & Bekaert on regime shifts, Clarke et al. on risk parity methods, and Ilmanen's comprehensive analysis of expected returns across asset classes.
Advanced practitioners will benefit from Roncalli's technical treatment of risk parity mathematics, Asness et al.'s academic critique of leverage aversion, and ongoing research in the Journal of Portfolio Management.
Golden Launch Pad🔰 Golden Launch Pad
This indicator identifies high-probability bullish setups by analyzing the relationship between multiple moving averages (MAs). A “Golden Launch Pad” is formed when the following five conditions are met simultaneously:
📌 Launch Pad Criteria (all must be true):
MAs Are Tightly Grouped
The selected MAs must be close together, measured using the Z-score spread — the difference between the highest and lowest Z-scores of the MAs.
Z-scores are calculated relative to the average and standard deviation of price over a user-defined window.
This normalizes MA distance based on volatility, making the signal adaptive across different assets.
MAs Are Bullishly Stacked
The MAs must be in strict ascending order: MA1 > MA2 > MA3 > ... > MA(n).
This ensures the short-term trend leads the longer-term trend — a classic sign of bullish structure.
All MAs Have Positive Slope
Each MA must be rising, based on a lookback period that is a percentage of its length (e.g. 30% of the MA’s bars).
This confirms momentum and avoids signals during sideways or weakening trends.
Price Is Above the Fastest MA
The current close must be higher than the first (fastest) moving average.
This adds a momentum filter and reduces false positives.
Price Is Near the MA Cluster
The current price must be close to the average of all selected MAs.
Proximity is measured in standard deviations (e.g. within 1.0), ensuring the price hasn't already made a large move away from the setup zone.
⚙️ Customization Options:
Use 2 to 6 MAs for the stack
Choose from SMA, EMA, WMA, VWMA for each MA
Adjustable Z-score window and spread threshold
Dynamic slope lookback based on MA length
Volatility-adjusted price proximity filter
🧠 Use Case:
This indicator helps traders visually and systematically detect strong continuation setups — often appearing before breakouts or sustained uptrends. It works well on intraday, swing, and positional timeframes across all asset classes.
For best results, combine with volume, breakout structure, or multi-timeframe confirmation.
TSI Indicator with Trailing StopAuthor: ProfitGang
Type: Indicator (visual + alerts). No orders are executed.
What it does
This tool combines the True Strength Index (TSI) with a simple tick-based trailing stop visualizer.
It plots buy/sell markers from a TSI cross with momentum confirmation and, if enabled, draws a trailing stop line that “ratchets” in your favor. It also shows a compact info table (position state, entry price, trailing status, and unrealized ticks).
Signal logic (summary)
TSI is computed with double EMA smoothing (user lengths).
Signals:
Buy when TSI crosses above its signal line and momentum (TSI–Signal histogram) improves, with TSI above your Buy Threshold.
Sell when TSI crosses below its signal line and momentum weakens, with TSI below your Sell Threshold.
Confirmation: Optional “Confirm on bar close” setting evaluates signals on closed bars to reduce repaint risk.
Trailing stop (visual only)
Units are ticks (uses the symbol’s min tick).
Start Trailing After (ticks): activates the trail only once price has moved in your favor by the set amount.
Trailing Stop (ticks): distance from price once active.
For longs: stop = close - trail; it never moves down.
For shorts: stop = close + trail; it never moves up.
Exits shown on chart when the trailing line is touched or an opposite signal occurs.
Note: This is a simulation for visualization and does not place, manage, or guarantee broker orders.
Inputs you can tune
TSI Settings: Long Length, Short Length, Signal Length, Buy/Sell thresholds, Confirm on Close.
Trailing Stop: Start Trailing After (ticks), Trailing Stop (ticks), Show/Hide trailing lines.
Display: Toggle chart signals, info table, and (optionally) TSI plots on the price chart.
Alerts included
TSI Buy / TSI Sell
Long/Short Trailing Activated
Long/Short Trail Exit
Tips for use
Timeframes/markets: Works on any symbol/timeframe that reports a valid min tick. If your market has large ticks, adjust the tick inputs accordingly.
TSI view: By default, TSI lines are hidden to avoid rescaling the price chart. Enable “Show TSI plots on price chart” if you want to see the oscillator inline.
Non-repainting note: With Confirm on bar close enabled, signals are evaluated on closed bars. Intrabar previews can change until the bar closes—this is expected behavior in TradingView.
Limitations
This is an indicator for education/research. It does not execute trades, and visuals may differ from actual broker fills.
Performance varies by market conditions; thresholds and trail settings should be tested by the user.
Disclaimer
Nothing here is financial advice. Markets involve risk, including possible loss of capital. Always do your own research and test on a demo before using any tool in live trading.
— ProfitGang
Gemini Trend Following SystemStrategy Description: The Gemini Trend Following System
Core Philosophy
This is a long-term trend-following system designed for a position trader or a patient swing trader, not a day trader. The fundamental goal is to capture the majority of a stock's major, multi-month or even multi-year uptrend.
The core principle is: "Buy weakness in a confirmed uptrend, and sell only when the uptrend's structure is fundamentally broken."
It operates on the belief that it's more profitable to ride a durable trend than to chase short-term breakouts or worry about daily price fluctuations. It prioritizes staying in a winning trade over frequent trading.
The Three Pillars of the Strategy
The script's logic is built on three distinct pillars, processed in order:
1. The Regime Filter: "Is This Stock in a Healthy Uptrend?"
Before even considering a trade, the script acts as a strict gatekeeper. It will only "watch" a stock if it meets all the criteria of a healthy, long-term uptrend. This is the most important part of the strategy as it filters out weak or speculative stocks.
A stock passes this filter if:
The 50-day Simple Moving Average (SMA) is above the 200-day SMA. This is the classic definition of a "Golden Cross" state, indicating the medium-term trend is stronger than the long-term trend—a hallmark of a bull market for the stock.
The stock's performance over the last year is positive. The Rate of Change (ROC) must be above a minimum threshold (e.g., 15%). This ensures we are only looking at stocks that have already demonstrated significant strength.
The 200-day SMA itself is rising. This is a crucial check to ensure the very foundation of the trend is solid and not flattening out or beginning to decline.
If a stock doesn't meet these conditions, the script ignores it completely.
2. The Entry Trigger: "When to Buy the Dip"
Once a stock is confirmed to be in a healthy uptrend, the script does not buy immediately. Instead, it patiently waits for a point of lower risk and higher potential reward—a pullback.
The entry trigger is a specific, two-step sequence:
The stock price first dips and closes below its 50-day SMA. This signifies a period of temporary weakness or profit-taking.
The price then recovers and closes back above the 50-day SMA within a short period (10 bars).
This sequence is a powerful signal. It suggests that institutional buyers view the 50-day SMA as a key support level and have stepped in to defend it, overpowering the sellers. The entry occurs at this point of confirmed support, marking the likely resumption of the uptrend. On the chart, this event is highlighted with a teal background.
3. The Exit Strategy: "When is the Trend Over?"
The exit logic is designed to keep you in the trade as long as possible and only sell when the trend's character has fundamentally changed. It uses a dual-exit system:
Primary Exit (Trend Failure): The main reason to sell is a "Death Cross"—when the 50-day SMA crosses below the 200-day SMA. This is a robust, albeit lagging, signal that the long-term uptrend is over and a bearish market structure is taking hold. This exit condition is designed to ignore normal market corrections and only trigger when the underlying trend has truly broken. On the chart, this is highlighted with a maroon background.
Safety-Net Exit (Catastrophic Stop-Loss): To protect against a sudden market crash or a company-specific disaster, a "safety-net" stop-loss is placed at the time of entry. This stop is set far below the entry price, typically underneath the 200-day SMA. It is a "just-in-case" measure that should only be triggered in a severe and rapid decline, protecting your capital from an unexpected black swan event.
Who is This Strategy For?
Position Traders: Investors who are comfortable holding a stock for many months to over a year.
Patient Swing Traders: Traders who want to capture large price swings over weeks and months, not days.
Investors using a Rules-Based Approach: Anyone looking to apply a disciplined, non-emotional system to their long-term portfolio.
Ideal Market Conditions
This strategy excels in markets with clear, durable trends. It performs best on strong, leading stocks during a sustained bull market. It will underperform significantly or generate losses in choppy, sideways, or range-bound markets, where the moving averages will frequently cross back and forth, leading to "whipsaw" trades.
Trading Report Generator from CSVMany people use the Trading Panel. Unfortunately, it doesn't have a Performance Report. However, TradingView has strategies, and they have a Performance Report :-D
What if we combine the first and second? It's easy!
This script is a special strategy that parses transactions in csv format from Paper Trading (and it will also work for other brokers) and “plays” them. As a result, we get a Performance Report for a specific instrument based on our real trades in Paper or another broker.
How to use it :
First, we need to get a CSV file with transactions. To do this, go to the Trading Panel and connect the desired broker. Select the History tab, then the Filled sub-tab, and configure the columns there, leaving only: Side, Qty, Fill Price, Closing Time. After that, open the Export data dialog, select History, and click Export. Open the downloaded CSV file in a regular text editor (Notepad or similar). It will contain a text like this:
Symbol,Side,Qty,Fill Price,Closing Time
FX:EURUSD,Buy,1000,1.0938700000000001,2023-04-05 14:29:23
COINBASE:ETHUSD,Sell,1,1332.05,2023-01-11 17:41:33
CME_MINI:ESH2023,Sell,1,3961.75,2023-01-11 17:30:40
CME_MINI:ESH2023,Buy,1,3956.75,2023-01-11 17:08:53
Next select all the text (Ctrl+A) and copy it to the clipboard.
Now apply the "Trading Report Generator from CSV" strategy to the chart with the desired symbol and TF, open the settings/input dialog, paste the contents of the clipboard into the single text input field of the strategy, and click Ok.
That's it.
In the Strategy Tester, we see a detailed Performance Report based on our real transactions.
P.S. The CSV file may contain transactions for different instruments, for example, you may have transactions for CRYPTO:BTCUSD and NASDAQ:AAPL. To view the report is based on CRYPTO:BTCUSD trades, simply change the symbol on the chart to CRYPTO:BTCUSD. To view the report is based on NASDAQ:AAPL trades, simply change the symbol on the chart to NASDAQ:AAPL. No changes to the strategy are required.
How it works :
At the beginning of the calculation, we parse the csv once, create trade objects (Trade) and sort them in chronological order. Next, on each bar, we check whether we have trades for the time period of the next bar. If there are, we place a limit order for each trade, with limit price == Fill Price of the trade. Here, we assume that if the trade is real, its execution price will be within the bar range, and the Pine strategy engine will execute this order at the specified limit price.
Tsallis Entropy Market RiskTsallis Entropy Market Risk Indicator
What Is It?
The Tsallis Entropy Market Risk Indicator is a market analysis tool that measures the degree of randomness or disorder in price movements. Unlike traditional technical indicators that focus on price patterns or momentum, this indicator takes a statistical physics approach to market analysis.
Scientific Foundation
The indicator is based on Tsallis entropy, a generalization of traditional Shannon entropy developed by physicist Constantino Tsallis. The Tsallis entropy is particularly effective at analyzing complex systems with long-range correlations and memory effects—precisely the characteristics found in crypto and stock markets.
The indicator also borrows from Log-Periodic Power Law (LPPL).
Core Concepts
1. Entropy Deficit
The primary measurement is the "entropy deficit," which represents how far the market is from a state of maximum randomness:
Low Entropy Deficit (0-0.3): The market exhibits random, uncorrelated price movements typical of efficient markets
Medium Entropy Deficit (0.3-0.5): Some patterns emerging, moderate deviation from randomness
High Entropy Deficit (0.5-0.7): Strong correlation patterns, potentially indicating herding behavior
Extreme Entropy Deficit (0.7-1.0): Highly ordered price movements, often seen before significant market events
2. Multi-Scale Analysis
The indicator calculates entropy across different timeframes:
Short-term Entropy (blue line): Captures recent market behavior (20-day window)
Long-term Entropy (green line): Captures structural market behavior (120-day window)
Main Entropy (purple line): Primary measurement (60-day window)
3. Scale Ratio
This measures the relationship between long-term and short-term entropy. A healthy market typically has a scale ratio above 0.85. When this ratio drops below 0.85, it suggests abnormal relationships between timeframes that often precede market dislocations.
How It Works
Data Collection: The indicator samples price returns over specific lookback periods
Probability Distribution Estimation: It creates a histogram of these returns to estimate their probability distribution
Entropy Calculation: Using the Tsallis q-parameter (typically 1.5), it calculates how far this distribution is from maximum entropy
Normalization: Results are normalized against theoretical maximum entropy to create the entropy deficit measure
Risk Assessment: Multiple factors are combined to generate a composite risk score and classification
Market Interpretation
Low Risk Environments (Risk Score < 25)
Market is functioning efficiently with reasonable randomness
Price discovery is likely effective
Normal trading and investment approaches appropriate
Medium Risk Environments (Risk Score 25-50)
Increasing correlation in price movements
Beginning of trend formation or momentum
Time to monitor positions more closely
High Risk Environments (Risk Score 50-75)
Strong herding behavior present
Market potentially becoming one-sided
Consider reducing position sizes or implementing hedges
Extreme Risk Environments (Risk Score > 75)
Highly ordered market behavior
Significant imbalance between buyers and sellers
Heightened probability of sharp reversals or corrections
Practical Application Examples
Market Tops: Often characterized by gradually increasing entropy deficit as momentum builds, followed by extreme readings near the actual top
Market Bottoms: Can show high entropy deficit during capitulation, followed by normalization
Range-Bound Markets: Typically display low and stable entropy deficit measurements
Trending Markets: Often show moderate entropy deficit that remains relatively consistent
Advantages Over Traditional Indicators
Forward-Looking: Identifies changing market structure before price action confirms it
Statistical Foundation: Based on robust mathematical principles rather than empirical patterns
Adaptability: Functions across different market regimes and asset classes
Noise Filtering: Focuses on meaningful structural changes rather than price fluctuations
Limitations
Not a Timing Tool: Signals market risk conditions, not precise entry/exit points
Parameter Sensitivity: Results can vary based on the chosen parameters
Historical Context: Requires some historical perspective to interpret effectively
Complementary Tool: Works best alongside other analysis methods
Enjoy :)
Canuck Trading Trader StrategyCanuck Trading Trader Strategy
Overview
The Canuck Trading Trader Strategy is a high-performance, trend-following trading system designed for NASDAQ:TSLA on a 15-minute timeframe. Optimized for precision and profitability, this strategy leverages short-term price trends to capture consistent gains while maintaining robust risk management. Ideal for traders seeking an automated, data-driven approach to trading Tesla’s volatile market, it delivers strong returns with controlled drawdowns.
Key Features
Trend-Based Entries: Identifies short-term trends using a 2-candle lookback period and a minimum trend strength of 0.2%, ensuring responsive trade signals.
Risk Management: Includes a configurable 3.0% stop-loss to cap losses and a 2.0% take-profit to lock in gains, balancing risk and reward.
High Precision: Utilizes bar magnification for accurate backtesting, reflecting realistic trade execution with 1-tick slippage and 0.1 commission.
Clean Interface: No on-chart indicators, providing a distraction-free trading experience focused on performance.
Flexible Sizing: Allocates 10% of equity per trade with support for up to 2 simultaneous positions (pyramiding).
Performance Highlights
Backtested from March 1, 2024, to June 20, 2025, on NASDAQ:TSLA (15-minute timeframe) with $1,000,000 initial capital:
Net Profit: $2,279,888.08 (227.99%)
Win Rate: 52.94% (3,039 winning trades out of 5,741)
Profit Factor: 3.495
Max Drawdown: 2.20%
Average Winning Trade: $1,050.91 (0.55%)
Average Losing Trade: $338.20 (0.18%)
Sharpe Ratio: 2.468
Note: Past performance is not indicative of future results. Always validate with your own backtesting and forward testing.
Usage Instructions
Setup:
Apply the strategy to a NASDAQ:TSLA 15-minute chart.
Ensure your TradingView account supports bar magnification for accurate results.
Configuration:
Lookback Candles: Default is 2 (recommended).
Min Trend Strength: Set to 0.2% for optimal trade frequency.
Stop Loss: Default 3.0% to cap losses.
Take Profit: Default 2.0% to secure gains.
Order Size: 10% of equity per trade.
Pyramiding: Allows up to 2 orders.
Commission: Set to 0.1.
Slippage: Set to 1 tick.
Enable "Recalculate After Order is Filled" and "Recalculate on Every Tick" in backtest settings.
Backtesting:
Run backtests over March 1, 2024, to June 20, 2025, to verify performance.
Adjust stop-loss (e.g., 2.5%) or take-profit (e.g., 1–3%) to suit your risk tolerance.
Live Trading:
Use with a compatible broker or TradingView alerts for automated execution.
Monitor execution for slippage or latency, especially given the high trade frequency (5,741 trades).
Validate in a demo account before deploying with real capital.
Risk Disclosure
Trading involves significant risk and may result in losses exceeding your initial capital. The Canuck Trading Trader Strategy is provided for educational and informational purposes only. Users are responsible for their own trading decisions and should conduct thorough testing before using in live markets. The strategy’s high trade frequency requires reliable execution infrastructure to minimize slippage and latency.
