Delta Volume BubblesDelta Volume Bubbles
Overview
The Delta Volume Bubbles indicator is an advanced order flow visualization tool that displays buying and selling pressure through dynamic bubble representations on your chart. Unlike traditional volume indicators that only show total volume, this indicator calculates the net delta volume (difference between buying and selling volume) and presents it as color-coded bubbles of varying sizes.
How It Works
Core Calculation Method
The indicator uses a sophisticated approach to estimate delta volume from standard OHLCV data:
1. Price Action Analysis: Analyzes the relationship between open, high, low, and close prices to determine market aggression
2. Body Ratio Calculation: body_ratio = |close - open| / (high - low)
3. Aggressive Factor: Applies multipliers based on price action:
- Strong moves (body_ratio > 0.7): 1.5x multiplier
- Moderate moves (body_ratio > 0.4): 1.2x multiplier
- Weak moves: 1.0x multiplier
4. Delta Volume Estimation:
- Buy Volume: price_change > 0 ? volume × aggressive_factor : 0
- Sell Volume: price_change < 0 ? volume × aggressive_factor : 0
- Net Delta: buy_volume - sell_volume
5. Delta Strength Normalization: delta_strength = |net_delta| / sma(volume, 20)
Percentile-Based Filtering
The indicator uses percentile filtering instead of fixed thresholds, making it adaptive to market conditions:
- Bubble Filter: Only shows bubbles when volume exceeds the specified percentile (default: 60%)
- Label Filter: Only displays numbers when volume exceeds a higher percentile (default: 90%)
- Dynamic Adaptation: Automatically adjusts to changing market volatility
Visual Elements
Bubble Sizes
- Tiny: Delta strength < 0.3
- Small: Delta strength 0.3 - 0.7
- Normal: Delta strength 0.7 - 1.2
- Large: Delta strength 1.2 - 2.0
- Huge: Delta strength > 2.0
Color Coding
- Aggressive Buy (Bright Green): Strong buying pressure with high body ratio
- Aggressive Sell (Bright Red): Strong selling pressure with high body ratio
- Passive Buy (Light Green): Moderate buying pressure
- Passive Sell (Light Red): Moderate selling pressure
Intensity Mode
Alternative coloring based on delta strength rather than flow direction:
- Gray: Low intensity (< 0.5)
- Blue: Medium intensity (0.5 - 1.0)
- Orange: High intensity (1.0 - 2.0)
- Red: Extreme intensity (> 2.0)
Parameters
Order Flow Settings
- Show Bubbles: Toggle bubble display on/off
- Bubble Volume %ile: Percentile threshold for bubble display (0-100%)
- Intensity Mode: Switch between flow-based and intensity-based coloring
Bubble Labels
- Show Numbers in Bubbles: Toggle numerical labels on/off
- Label Volume %ile: Higher percentile threshold for label display (0-100%)
Numbers are displayed in K-notation (e.g., 25000 → 25K, 1500000 → 1.5M) for better readability.
Ideal Usage Scenarios
Best Market Conditions
- High volume sessions: More accurate delta calculations
- Trending markets: Clear directional flow identification
- Breakout scenarios: Spot aggressive buying/selling at key levels
- Support/resistance testing: Identify accumulation vs distribution
Trading Applications
1. Entry Timing: Look for aggressive flow in your trade direction
2. Exit Signals: Watch for opposing aggressive flow
3. Trend Confirmation: Consistent flow direction confirms trends
4. Volume Climax: Huge bubbles may indicate exhaustion points
Optimization Tips
Parameter Adjustment
- Lower percentiles (40-60%): More bubbles, good for active markets
- Higher percentiles (70-90%): Fewer bubbles, focus on significant events
- Label percentile: Set 20-30% higher than bubble percentile for clarity
Visual Optimization
- Intensity mode: Better for identifying unusual volume spikes
- Flow mode: Better for directional bias analysis
- Label toggle: Turn off in crowded markets, on for key levels
Limitations
- Estimation-based: Uses approximation algorithms, not true order flow data
- Volume dependency: Requires accurate volume data to function properly
- Timeframe sensitivity: Works best on intraday timeframes with active volume
- Market hours: Most effective during high-volume trading sessions
Technical Notes
The indicator implements advanced Pine Script features including:
- Dynamic percentile calculations using ta.percentile_linear_interpolation()
- Conditional plotting with multiple size categories
- Custom number formatting functions
- Efficient label management to prevent display limits
This tool is designed for traders who want to understand the underlying buying and selling pressure beyond simple volume analysis, providing insights into market sentiment and potential turning points.
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Position Size Calculator with Fees# Position Size Calculator with Portfolio Management - Manual
## Overview
The Position Size Calculator with Portfolio Management is an advanced Pine Script indicator designed to help traders calculate optimal position sizes based on their total portfolio value and risk management strategy. This tool automatically calculates your risk amount based on portfolio allocation percentages and determines the exact position size needed while accounting for trading fees.
## Key Features
- **Portfolio-Based Risk Management**: Calculates risk based on total portfolio value
- **Tiered Risk Allocation**: Separates trading allocation from total portfolio
- **Automatic Trade Direction Detection**: Determines long/short based on entry vs stop loss
- **Fee Integration**: Accounts for trading fees in position size calculations
- **Risk Factor Adjustment**: Allows scaling of position size up or down
- **Visual Display**: Shows all calculations in a clear, color-coded table
- **Automatic Risk Calculation**: No need to manually input risk amount
## Input Parameters
### Total Portfolio ($)
- **Purpose**: The total value of your investment portfolio
- **Default**: 0.0
- **Range**: Any positive value
- **Step**: 0.01
- **Example**: If your total portfolio is worth $100,000, enter 100000
### Trading Portfolio Allocation (%)
- **Purpose**: The percentage of your total portfolio allocated to active trading
- **Default**: 20.0%
- **Range**: 0.0% to 100.0%
- **Step**: 0.01
- **Example**: If you allocate 20% of your portfolio to trading, enter 20
### Risk from Trading (%)
- **Purpose**: The percentage of your trading allocation you're willing to risk per trade
- **Default**: 0.1%
- **Range**: Any positive value
- **Step**: 0.01
- **Example**: If you risk 0.1% of your trading allocation per trade, enter 0.1
### Entry Price ($)
- **Purpose**: The price at which you plan to enter the trade
- **Default**: 0.0
- **Range**: Any positive value
- **Step**: 0.01
### Stop Loss ($)
- **Purpose**: The price at which you will exit if the trade goes against you
- **Default**: 0.0
- **Range**: Any positive value
- **Step**: 0.01
### Risk Factor
- **Purpose**: A multiplier to scale your position size up or down
- **Default**: 1.0 (no scaling)
- **Range**: 0.0 to 10.0
- **Step**: 0.1
- **Examples**:
- 1.0 = Normal position size
- 2.0 = Double the position size
- 0.5 = Half the position size
### Fee (%)
- **Purpose**: The percentage fee charged per transaction
- **Default**: 0.01% (0.01)
- **Range**: 0.0% to 1.0%
- **Step**: 0.001
## How Risk Amount is Calculated
The script automatically calculates your risk amount using this formula:
```
Risk Amount = Total Portfolio × Trading Allocation (%) × Risk % ÷ 10,000
```
### Example Calculation:
- Total Portfolio: $100,000
- Trading Allocation: 20%
- Risk per Trade: 0.1%
**Risk Amount = $100,000 × 20 × 0.1 ÷ 10,000 = $20**
This means you would risk $20 per trade, which is 0.1% of your $20,000 trading allocation.
## Portfolio Structure Example
Let's say you have a $100,000 portfolio:
### Allocation Structure:
- **Total Portfolio**: $100,000
- **Trading Allocation (20%)**: $20,000
- **Long-term Investments (80%)**: $80,000
### Risk Management:
- **Risk per Trade (0.1% of trading)**: $20
- **Maximum trades at risk**: Could theoretically have 1,000 trades before risking entire trading allocation
## How Position Size is Calculated
### Trade Direction Detection
- **Long Trade**: Entry price > Stop loss price
- **Short Trade**: Entry price < Stop loss price
### Position Size Formulas
#### For Long Trades:
```
Position Size = -Risk Factor × Risk Amount / (Stop Loss × (1 - Fee) - Entry Price × (1 + Fee))
```
#### For Short Trades:
```
Position Size = -Risk Factor × Risk Amount / (Entry Price × (1 - Fee) - Stop Loss × (1 + Fee))
```
## Output Display
The indicator displays a comprehensive table with color-coded sections:
### Portfolio Information (Light Blue Background)
- **Portfolio (USD)**: Your total portfolio value
- **Trading Portfolio Allocation (%)**: Percentage allocated to trading
- **Risk as % of Trading**: Risk percentage per trade
### Trade Setup (Gray Background)
- **Entry Price**: Your specified entry price
- **Stop Loss**: Your specified stop loss price
- **Fee (%)**: Trading fee percentage
- **Risk Factor**: Position size multiplier
### Risk Analysis (Red Background)
- **Risk Amount**: Automatically calculated dollar risk
- **Effective Entry**: Actual entry cost including fees
- **Effective Exit**: Actual exit value including fees
- **Expected Loss**: Calculated loss if stop loss is hit
- **Deviation from Risk %**: Accuracy of risk calculation
### Final Result (Blue Background)
- **Position Size**: Number of shares/units to trade
## Usage Examples
### Example 1: Conservative Long Trade
- **Total Portfolio**: $50,000
- **Trading Allocation**: 15%
- **Risk per Trade**: 0.05%
- **Entry Price**: $25.00
- **Stop Loss**: $24.00
- **Risk Factor**: 1.0
- **Fee**: 0.01%
**Calculated Risk Amount**: $50,000 × 15% × 0.05% ÷ 100 = $3.75
### Example 2: Aggressive Short Trade
- **Total Portfolio**: $200,000
- **Trading Allocation**: 30%
- **Risk per Trade**: 0.2%
- **Entry Price**: $150.00
- **Stop Loss**: $155.00
- **Risk Factor**: 2.0
- **Fee**: 0.01%
**Calculated Risk Amount**: $200,000 × 30% × 0.2% ÷ 100 = $120
**Actual Risk**: $120 × 2.0 = $240 (due to risk factor)
## Color Coding System
- **Green/Red Header**: Trade direction (Long/Short)
- **Light Blue**: Portfolio management parameters
- **Gray**: Trade setup parameters
- **Red**: Risk-related calculations and results
- **Blue**: Final position size result
## Best Practices
### Portfolio Management
1. **Keep trading allocation reasonable** (typically 10-30% of total portfolio)
2. **Use conservative risk percentages** (0.05-0.2% per trade)
3. **Don't risk more than you can afford to lose**
### Risk Management
1. **Start with small risk factors** (1.0 or less) until comfortable
2. **Monitor your total exposure** across all open positions
3. **Adjust risk based on market conditions**
### Trade Execution
1. **Always validate calculations** before placing trades
2. **Account for slippage** in volatile markets
3. **Consider position size relative to liquidity**
## Risk Management Guidelines
### Conservative Approach
- Trading Allocation: 10-20%
- Risk per Trade: 0.05-0.1%
- Risk Factor: 0.5-1.0
### Moderate Approach
- Trading Allocation: 20-30%
- Risk per Trade: 0.1-0.15%
- Risk Factor: 1.0-1.5
### Aggressive Approach
- Trading Allocation: 30-40%
- Risk per Trade: 0.15-0.25%
- Risk Factor: 1.5-2.0
## Troubleshooting
### Common Issues
1. **Position Size shows 0**
- Verify all portfolio inputs are greater than 0
- Check that entry price differs from stop loss
- Ensure calculated risk amount is positive
2. **Very small position sizes**
- Increase risk percentage or risk factor
- Check if your risk amount is too small for the price difference
3. **Large risk deviation**
- Normal for very small positions
- Consider adjusting entry/stop loss levels
### Validation Checklist
- Total portfolio value is realistic
- Trading allocation percentage makes sense
- Risk percentage is conservative
- Entry and stop loss prices are valid
- Trade direction matches your intention
## Advanced Features
### Risk Factor Usage
- **Scaling up**: Use risk factors > 1.0 for high-confidence trades
- **Scaling down**: Use risk factors < 1.0 for uncertain trades
- **Never exceed**: Risk factors that would risk more than your comfort level
### Multiple Timeframe Analysis
- Use different risk factors for different timeframes
- Consider correlation between positions
- Adjust trading allocation based on market conditions
## Disclaimer
This tool is for educational and planning purposes only. Always verify calculations manually and consider market conditions, liquidity, and correlation between positions. The automated risk calculation assumes you're comfortable with the mathematical relationship between portfolio allocation and individual trade risk. Past performance doesn't guarantee future results, and all trading involves risk of loss.
Easy Position Size Calculator with Fees# Easy Position Size Calculator with Fees - Manual
## Overview
The Easy Position Size Calculator is a Pine Script indicator designed to help traders calculate the optimal position size for their trades while accounting for trading fees. This tool automatically determines whether you're planning a long or short position and calculates the exact position size needed to risk a specific dollar amount.
## Key Features
- **Automatic Trade Direction Detection**: Determines if you're going long or short based on entry price vs stop loss
- **Fee Integration**: Accounts for trading fees in position size calculations
- **Risk Management**: Calculates position size based on your specified risk amount
- **Risk Factor Adjustment**: Allows you to scale your position size up or down
- **Visual Display**: Shows all calculations in a clear, organized table
## Input Parameters
### Entry Price ($)
- **Purpose**: The price at which you plan to enter the trade
- **Default**: 0.0
- **Range**: Any positive value
- **Step**: 0.01
### Stop Loss ($)
- **Purpose**: The price at which you will exit the trade if it goes against you
- **Default**: 0.0
- **Range**: Any positive value
- **Step**: 0.01
### Risk ($)
- **Purpose**: The maximum dollar amount you're willing to lose on this trade
- **Default**: 0.0
- **Range**: Any positive value
- **Step**: 0.01
### Risk Factor
- **Purpose**: A multiplier to scale your position size up or down
- **Default**: 1.0 (no scaling)
- **Range**: 0.0 to 10.0
- **Step**: 0.1
- **Examples**:
- 1.0 = Normal position size
- 2.0 = Double the position size
- 0.5 = Half the position size
### Fee (%)
- **Purpose**: The percentage fee charged per transaction (buy/sell)
- **Default**: 0.01% (0.01)
- **Range**: 0.0% to 1.0%
- **Step**: 0.001
## How It Works
### Trade Direction Detection
The script automatically determines your trade direction:
- **Long Trade**: Entry price > Stop loss price
- **Short Trade**: Entry price < Stop loss price
### Position Size Calculation
#### For Long Trades:
```
Position Size = -Risk Factor × Risk Amount / (Stop Loss × (1 - Fee) - Entry Price × (1 + Fee))
```
#### For Short Trades:
```
Position Size = -Risk Factor × Risk Amount / (Entry Price × (1 - Fee) - Stop Loss × (1 + Fee))
```
### Fee Adjustment
The script accounts for fees on both entry and exit:
- **Long trades**: You pay fees when buying (entry) and selling (exit)
- **Short trades**: You pay fees when shorting (entry) and covering (exit)
## Output Display
The indicator displays a table with the following information:
### Trade Information
- **Trade Type**: Shows whether it's a LONG, SHORT, or INVALID trade
- **Entry Price**: Your specified entry price
- **Stop Loss**: Your specified stop loss price
- **Fee (%)**: The fee percentage being used
### Risk Parameters
- **Risk Amount**: The dollar amount you're willing to risk
- **Risk Factor**: The multiplier being applied
### Calculated Values
- **Effective Entry**: The actual cost per share including fees
- **Effective Exit**: The actual exit value per share including fees
- **Expected Loss**: The calculated loss if stop loss is hit
- **Deviation from Risk %**: Shows how close the expected loss is to your target risk
- **Position Size**: The number of shares/units to trade
## Usage Examples
### Example 1: Long Trade
- Entry Price: $100.00
- Stop Loss: $95.00
- Risk Amount: $500.00
- Risk Factor: 1.0
- Fee: 0.01%
**Result**: The script will calculate how many shares to buy so that if the stop loss is hit, you lose approximately $500 (accounting for fees). Position Size: 99.61152
### Example 2: Short Trade
- Entry Price: $50.00
- Stop Loss: $55.00
- Risk Amount: $300.00
- Risk Factor: 1.0
- Fee: 0.01%
**Result**: The script will calculate how many shares to short so that if the stop loss is hit, you lose approximately $300 (accounting for fees). Position Size: 59.87426
## Important Notes
### Validation Requirements
For the script to work properly, all of the following must be true:
- Entry price > 0
- Stop loss > 0
- Risk amount > 0
- Entry price ≠ Stop loss (to determine direction)
### Negative Position Sizes
The script may show negative position sizes, which is normal:
- **Negative values for long trades**: Represents shares to buy
- **Negative values for short trades**: Represents shares to short
### Risk Deviation
The "Deviation from Risk %" shows how closely the calculated position size matches your target risk. Small deviations are normal due to:
- Fee calculations
- Rounding
- Market precision
## Color Coding
The table uses color coding for easy identification:
- **Green**: Long trade information
- **Red**: Short trade information
- **Gray**: Invalid trade (when inputs are incorrect)
- **Blue**: Final position size
- **Red background**: Risk-related calculations
## Troubleshooting
### Common Issues
1. **Position Size shows 0**
- Check that all inputs are greater than 0
- Ensure entry price is different from stop loss
2. **Trade Type shows INVALID**
- Verify that entry price and stop loss are both positive
- Make sure entry price ≠ stop loss
3. **Large Risk Deviation**
- This is normal for very small position sizes
- Consider adjusting your risk amount or price levels
## Best Practices
1. **Always validate your inputs** before placing actual trades
2. **Double-check the trade direction** shown in the table
3. **Review the expected loss** to ensure it aligns with your risk management
4. **Consider the effective entry/exit prices** which include fees
5. **Use appropriate risk factors** - avoid extreme values that could lead to overexposure
## Disclaimer
This tool is for educational and planning purposes only. Always verify calculations manually and consider market conditions, liquidity, and other factors before placing actual trades. The script assumes that fees are charged on both entry and exit transactions.
Warrior Trading Momentum Strategy
# 🚀 Warrior Trading Momentum Strategy - Day Trading Excellence
## Strategy Overview
This comprehensive Pine Script strategy replicates the proven methodologies taught by Ross Cameron and the Warrior Trading community. Designed for active day traders, it identifies high-probability momentum setups with strict risk management protocols.
## 📈 Core Trading Setups
### 1. Gap and Go Trading
- **Primary Focus**: Stocks gapping up 2%+ with volume confirmation
- **Entry Logic**: Breakout above gap open with momentum validation
- **Volume Filter**: 2x average volume requirement for quality setups
### 2. ABCD Pattern Recognition
- **Pattern Detection**: Automated identification of classic ABCD reversal patterns
- **Validation**: A-B and C-D move relationship analysis
- **Entry Trigger**: D-point breakout with volume confirmation
### 3. VWAP Momentum Plays
- **Strategy**: Entries near VWAP with bounce confirmation
- **Distance Filter**: Configurable percentage distance for optimal entries
- **Direction Bias**: Above VWAP bullish momentum validation
### 4. Red to Green Reversals
- **Setup**: Reversal patterns after consecutive red candles
- **Confirmation**: Volume spike with bullish close required
- **Momentum**: Trend change validation with RSI support
### 5. Breakout Momentum
- **Logic**: Breakouts above recent highs with volume
- **Filters**: EMA20 and RSI confirmation for quality
- **Trend**: Established momentum direction validation
## ⚡ Key Features
### Smart Risk Management
- **Position Sizing**: Automatic calculation based on account risk percentage
- **Stop Loss**: 2 ATR-based stops for volatility adjustment
- **Take Profit**: Configurable risk-reward ratios (default 1:2)
- **Trailing Stops**: Profit protection with adjustable triggers
### Advanced Filtering System
- **Time Filters**: Market hours trading with lunch hour avoidance
- **Volume Confirmation**: Multi-timeframe volume analysis
- **Momentum Indicators**: RSI and moving average trend validation
- **Quality Control**: Multiple confirmation layers for signal accuracy
### PDT-Friendly Design
- **Trade Limiting**: Built-in daily trade counter for accounts under $25K
- **Selective Trading**: Priority scoring system for A+ setups only
- **Quality over Quantity**: Maximum 2-3 high-probability trades per day
## 🎯 Optimal Usage
### Best Timeframes
- **Primary**: 5-minute charts for entry timing
- **Secondary**: 1-minute for precise execution
- **Context**: Daily charts for gap analysis
### Ideal Market Conditions
- **Volatility**: High-volume, momentum-driven markets
- **Stocks**: Market cap $100M+, average volume 1M+ shares
- **Sectors**: Technology, biotech, growth stocks with news catalysts
### Account Requirements
- **Minimum**: $500+ for proper position sizing
- **Recommended**: $25K+ for unlimited day trading
- **Risk Tolerance**: Active day trading experience preferred
## 📊 Performance Optimization
### Entry Criteria (All Must Align)
1. ✅ Time filter (market hours, avoid lunch)
2. ✅ Volume spike (2x+ average volume)
3. ✅ Momentum confirmation (RSI 50-80)
4. ✅ Trend alignment (above EMA20)
5. ✅ Pattern completion (setup-specific)
### Risk Parameters
- **Maximum Risk**: 1-2% per trade
- **Position Size**: 25% of account maximum
- **Stop Loss**: 2 ATR below entry
- **Take Profit**: 2:1 risk-reward minimum
## 🔧 Customization Options
### Gap Trading Settings
- Minimum gap percentage threshold
- Volume multiplier requirements
- Gap validation criteria
### Pattern Recognition
- ABCD ratio parameters
- Swing point sensitivity
- Pattern completion filters
### Risk Management
- Risk-reward ratio adjustment
- Maximum daily trade limits
- Trailing stop trigger levels
### Time and Session Filters
- Trading session customization
- Lunch hour avoidance toggle
- Market condition filters
## ⚠️ Important Disclaimers
### Risk Warning
- **High Risk**: Day trading involves substantial risk of loss
- **Capital Requirements**: Only trade with risk capital
- **Experience**: Strategy requires active monitoring and experience
- **Market Conditions**: Performance varies with market volatility
### PDT Considerations
- **Day Trading Rules**: Accounts under $25K limited to 3 day trades per 5 days
- **Compliance**: Strategy includes trade counting for PDT compliance
- **Alternative**: Consider swing trading modifications for smaller accounts
### Backtesting vs Live Trading
- **Slippage**: Real trading involves execution delays and slippage
- **Commissions**: Factor in broker fees for accurate performance
- **Market Impact**: Large positions may affect fill prices
- **Psychological Factors**: Live trading involves emotional challenges
## 📚 Educational Value
This strategy serves as an excellent learning tool for understanding:
- Professional day trading methodologies
- Risk management principles
- Pattern recognition techniques
- Volume and momentum analysis
- Multi-timeframe analysis
## 🤝 Community and Support
Based on proven Warrior Trading methodologies with active community support. Strategy includes comprehensive plotting and information tables for educational purposes and trade analysis.
---
**Disclaimer**: This strategy is for educational purposes. Past performance does not guarantee future results. Always practice proper risk management and never risk more than you can afford to lose.
**Tags**: #DayTrading #Momentum #WarriorTrading #GapAndGo #ABCD #VWAP #PatternTrading #RiskManagement
LANZ Strategy 1.0 [Backtest]🔷 LANZ Strategy 1.0 — Time-Based Session Trading with Smart Reversal Logic and Risk-Controlled Limit Orders
This backtest version of LANZ Strategy 1.0 brings precision to session-based trading by using directional confirmation, pre-defined risk parameters, and limit orders that execute overnight. Designed for the 1-hour timeframe, it allows traders to evaluate the system with configurable SL, TP, and risk settings in a fully automated environment.
🧠 Core Strategy Logic:
1. Directional Confirmation at 18:00 NY:
At 18:00 NY, the system compares the 08:00 open vs the 18:00 close:
If the direction matches the previous day, the signal is reversed.
If the direction differs, the current day's trend is kept.
This logic is designed to avoid momentum exhaustion and capture corrective reversals.
2. Entry Level Definition:
Based on the confirmed direction:
For BUY, the Low of the day is used as Entry Point (EP).
For SELL, the High of the day becomes EP.
The system plots a Stop Loss and Take Profit based on user-defined pip inputs (default: SL = 18 pips, TP = 54 pips → RR 1:3).
3. Time-Limited Entry Execution (LIMIT Orders):
Orders are sent after 18:00 NY and can be triggered anytime between 18:00 and 08:00 NY.
If EP is not touched before 08:00, the order is automatically cancelled.
4. Manual Close Feature:
If the trade is still open at the configured hour (default 09:00 NY), the system closes all positions, simulating realistic intraday exit scenarios.
5. Lot Size Calculation Based on Risk:
Lot size is dynamically calculated using the account size, risk percentage, and SL distance.
This ensures consistent risk exposure regardless of market volatility.
⚙️ Step-by-Step Flow:
08:00 NY → Captures the open of the day.
18:00 NY → Confirms direction and defines EP, SL, and TP.
After 18:00 NY → If conditions are met, a LIMIT order is placed at EP.
Between 18:00–08:00 NY → If price touches EP, the trade is executed.
At 08:00 NY → If EP wasn’t touched, the order is cancelled.
At Configured Manual Close Time (default 09:00 NY) → All open positions are force-closed if still active.
🧪 Backtest Settings:
Timeframe: 1-hour only
Order Type: strategy.entry() with limit=
SL/TP Configurable: Yes, in pips
Risk Input: % of capital per trade
Manual Close Time: Fully adjustable (default 09:00 NY)
👨💻 Credits:
Developed by LANZ
Strategy logic and trading concept built with clarity and precision.
Code structure and documentation by Kairos, your AI trading assistant.
Designed for high-confidence execution and clean backtesting performance.
LANZ Strategy 1.0🔷 LANZ Strategy 1.0 — Session-Based Directional Logic with Visual Multi-Account Risk Management
LANZ Strategy 1.0 is a structured and disciplined trading strategy designed for the 1-hour timeframe, operating during the NY session and executing trades overnight. It uses the directional behavior between 08:00 and 18:00 New York time to define precise limit entries for the following night. Ideal for traders who prefer time-based execution, clear visuals, and professional risk management across multiple accounts.
🧠 Core Components:
1. Session Direction Confirmation:
At 18:00 NY, the system evaluates the market direction by comparing the open at 08:00 vs the close at 18:00:
If the direction matches the previous day, it is reversed.
If it differs, the current day’s direction is kept.
This logic is designed to avoid trend exhaustion and favor potential reversal opportunities.
2. EP Level & Risk Definition:
Once direction is defined:
For BUY, EP is set at the Low of the session.
For SELL, EP is set at the High of the session.
The system automatically plots:
SL fixed at 18 pips from EP
TP at 3.00× the risk → 54 pips from EP
All levels (EP, SL, TP) are shown with visual lines and price labels.
3. Time-Restricted Entry Execution:
The entry is only valid if price touches the EP between 19:00 and 08:00 NY.
If EP is not touched before 08:00 NY, the trade is automatically cancelled.
4. Multi-Account Lot Sizing:
Traders can configure up to five different accounts, each with its own capital and risk percentage.
The system calculates and displays the lot size per account, based on SL distance and pip value, in a dynamic floating label.
5. Outcome Tracking:
If TP is hit, a +3.00% profit label is displayed along with a congratulatory alert.
If SL is hit, a -1.00% label appears with a loss alert.
If the trade is still open by 09:00 NY, it is manually closed, and the result is shown as a percentage of the initial risk.
📊 Visual Features:
Custom-colored angle and guide lines.
Dynamic angle line starts at 08:00 NY and tracks price until 18:00.
Shaded backgrounds for key time zones (e.g., 08:00, 18:00, 19:00).
BUY/SELL signals shown at 19:00 based on match/divergence logic.
Label panel showing risk metrics and lot size for each active account.
⚙️ How It Works:
08:00 NY: Marks the session open and initiates a dynamic angle line.
18:00 NY: Evaluates the session direction and calculates EP/SL/TP based on outcome.
19:00 NY: Activates limit order monitoring.
During the night (until 08:00 NY): If EP is touched, the trade is triggered.
At 08:00 NY: If no touch occurred, trade is cancelled.
Overnight: TP/SL logic is enforced, showing percentage outcomes.
At 09:00 NY: If still open, trade is closed manually and result is labeled visually.
🔔 Alerts:
🚀 EP execution alert when touched
💢 Stop Loss hit alert
⚡ Take Profit hit alert
✅ Manual close at 09:00 NY with performance result
🔔 Daily reminder at 19:00 NY to configure and prepare the trade
📝 Notes:
Strategy is exclusive to the 1-hour timeframe.
Works best on assets with clean NY session movement.
Perfect for structured, semi-automated swing/overnight trading styles.
Fully visual, self-explanatory, and backtest-friendly.
👨💻 Credits:
Developed by LANZ
A strategy created with precision, discipline, and a vision for traders who value time-based entries, clean execution logic, and visual confidence on the chart.
Special thanks to Kairos — your AI assistant — for the detailed structure, scripting, and documentation of the strategy.
Velocity + Momentum (SMA-Based)Velocity + Momentum (SMA-Based) is a clean, powerful oscillator that measures price acceleration using SMA-derived velocity and dual momentum signals. This tool is ideal for identifying directional shifts, exhaustion points, and early entries across any market or timeframe.
How It Works:
This indicator calculates velocity as the distance between the current close and a simple moving average of the open price. Then, it applies two smoothed moving averages to this velocity line:
• Internal Momentum (shorter-term smoothing)
• External Momentum (longer-term context, hidden by default)
The result is a layered view of how fast price is moving and whether that move is gaining or losing strength.
How to Use:
• The green/red histogram shows current velocity (positive = bullish, negative = bearish)
• The teal/maroon line tracks internal momentum and provides short-term signal turns
• The black/gray (hidden) line reflects external momentum and supports broader trend alignment
• Watch for crosses above/below the zero line for confirmation of directional strength
• Use the built-in alerts to catch real-time shifts in all three layers of movement: velocity, internal, and external
Why It's Useful:
• Detects subtle transitions before price structure changes
• Helps filter out noise by comparing short-term vs long-term motion
• Ideal for scalpers, swing traders, and trend-followers alike
• Pairs well with structure-based tools or price action zones
• Works on any asset and timeframe
This indicator simplifies momentum analysis by giving you actionable, multi-layered feedback on how price is accelerating — and when that’s likely to reverse.
Yelober_Momentum_BreadthMI# Yelober_Momentum_BreadthMI: Market Breadth Indicator Analysis
## Overview
The Yelober_Momentum_BreadthMI is a comprehensive market breadth indicator designed to monitor market internals across NYSE and NASDAQ exchanges. It tracks several key metrics including up/down volume ratios, TICK readings, and trend momentum to provide traders with real-time insights into market direction, strength, and potential turning points.
## Indicator Components
This indicator displays a table with data for:
- NYSE breadth metrics
- NASDAQ breadth metrics
- NYSE TICK data and trends
- NASDAQ TICK (TICKQ) data and trends
## Table Columns and Interpretation
### Column 1: Market
Identifies the data source:
- **NYSE**: New York Stock Exchange data
- **NASDAQ**: NASDAQ exchange data
- **Tick**: NYSE TICK index
- **TickQ**: NASDAQ TICK index
### Column 2: Ratio
Shows the current ratio values with different calculations depending on the row:
- **For NYSE/NASDAQ rows**: Displays the up/down volume ratio
- Positive values (green): More up volume than down volume
- Negative values (red): More down volume than up volume
- The magnitude indicates the strength of the imbalance
- **For Tick/TickQ rows**: Shows the ratio of positive to negative ticks plus the current TICK reading in parentheses
- Format: "Ratio (Current TICK value)"
- Positive values (green): More stocks ticking up than down
- Negative values (red): More stocks ticking down than up
### Column 3: Trend
Displays the directional trend with both a symbol and value:
- **For NYSE/NASDAQ rows**: Shows the VOLD (volume difference) slope
- "↗": Rising trend (positive slope)
- "↘": Falling trend (negative slope)
- "→": Neutral/flat trend (minimal slope)
- **For Tick/TickQ rows**: Shows the slope of the ratio history
- Color-coding: Green for positive momentum, Red for negative momentum, Gray for neutral
The trend column is particularly important as it shows the current momentum of the market. The indicator applies specific thresholds for color-coding:
- NYSE: Green when normalized value > 2, Red when < -2
- NASDAQ: Green when normalized value > 3.5, Red when < -3.5
- TICK/TICKQ: Green when slope > 0.01, Red when slope < -0.01
## How to Use This Indicator
### Basic Interpretation
1. **Market Direction**: When multiple rows show green ratios and upward trends, it suggests strong bullish market internals. Conversely, red ratios and downward trends indicate bearish internals.
2. **Market Breadth**: The magnitude of the ratios indicates how broad-based the market movement is. Higher absolute values suggest stronger market breadth.
3. **Momentum Shifts**: When trend arrows change direction or colors shift, it may signal a potential reversal or change in market momentum.
4. **Divergences**: Look for divergences between different markets (NYSE vs NASDAQ) or between ratios and trends, which can indicate potential market turning points.
### Advanced Usage
- **Volume Normalization**: The indicator includes options to normalize volume data (none, tens, thousands, millions, 10th millions) to handle different exchange scales.
- **Trend Averaging**: The slope calculation uses an averaging period (default: 5) to smooth out noise and identify more reliable trend signals.
## Examples for Interpretation
### Example 1: Strong Bullish Market
```
| Market | Ratio | Trend |
|--------|---------|-----------|
| NYSE | 1.75 | ↗ 2.85 |
| NASDAQ | 2.10 | ↗ 4.12 |
| Tick | 2.45 (485) | ↗ 0.05 |
| TickQ | 1.95 (320) | ↗ 0.03 |
```
**Interpretation**: All metrics are positive and trending upward (green), indicating a strong, broad-based rally. The high ratio values show significant bullish dominance. This suggests continuation of the upward move with good momentum.
### Example 2: Weakening Market
```
| Market | Ratio | Trend |
|--------|---------|-----------|
| NYSE | 0.45 | ↘ -1.50 |
| NASDAQ | 0.85 | → 0.30 |
| Tick | 0.95 (105) | ↘ -0.02 |
| TickQ | 1.20 (160) | → 0.00 |
```
**Interpretation**: The market is showing mixed signals with positive but low ratios, while NYSE and TICK trends are turning negative. NASDAQ shows neutral to slightly positive momentum. This divergence often occurs near market tops or during consolidation phases. Traders should be cautious and consider reducing position sizes.
### Example 3: Negative Market Turning Positive
```
| Market | Ratio | Trend |
|--------|---------|-----------|
| NYSE | -1.25 | ↗ 1.75 |
| NASDAQ | -0.95 | ↗ 2.80 |
| Tick | -1.35 (-250) | ↗ 0.04 |
| TickQ | -1.10 (-180) | ↗ 0.02 |
```
**Interpretation**: This is a potential bottoming pattern. Current ratios are still negative (red) showing overall negative breadth, but the trends are all positive (green arrows), indicating improving momentum. This divergence often occurs at market bottoms and could signal an upcoming reversal. Look for confirmation with price action before establishing long positions.
### Example 4: Mixed Market with Divergence
```
| Market | Ratio | Trend |
|--------|---------|-----------|
| NYSE | 1.45 | ↘ -2.25 |
| NASDAQ | -0.85 | ↘ -3.80 |
| Tick | 1.20 (230) | ↘ -0.03 |
| TickQ | -0.75 (-120) | ↘ -0.02 |
```
**Interpretation**: There's a significant divergence between NYSE (positive ratio) and NASDAQ (negative ratio), while all trends are negative. This suggests sector rotation or a market that's weakening but with certain segments still showing strength. Often seen during late-stage bull markets or in transitions between leadership groups. Consider reducing risk exposure and focusing on relative strength sectors.
## Practical Trading Applications
1. **Confirmation Tool**: Use this indicator to confirm price movements. Strong breadth readings in the direction of the price trend increase confidence in trade decisions.
2. **Early Warning System**: Watch for divergences between price and breadth metrics, which often precede market turns.
3. **Intraday Trading**: The real-time nature of TICK and volume data makes this indicator valuable for day traders to gauge intraday momentum shifts.
4. **Market Regime Identification**: Sustained readings can help identify whether the market is in a trend or chop regime, allowing for appropriate strategy selection.
This breadth indicator is most effective when used in conjunction with price action and other technical indicators rather than in isolation.
Trend Flow Trail [AlgoAlpha]OVERVIEW
This script overlays a custom hybrid indicator called the Money Flow Trail which combines a volatility-based trend-following trail with a volume-weighted momentum oscillator. It’s built around two core components: the AlphaTrail—a dynamic band system influenced by Hull MA and volatility—and a smoothed Money Flow Index (MFI) that provides insights into buying or selling pressure. Together, these tools are used to color bars, generate potential reversal markers, and assist traders in identifying trend continuation or exhaustion phases in any market or timeframe.
CONCEPTS
The AlphaTrail calculates a volatility-adjusted channel around price using the Hull Moving Average as the base and an EMA of range as the spread. It adaptively shifts based on price interaction to capture trend reversals while avoiding whipsaws. The direction (bullish or bearish) determines both the band being tracked and how the trail locks in. The Money Flow Index (MFI) is derived from hlc3 and volume, measuring buying vs selling pressure, and is further smoothed with a short Hull MA to reduce noise while preserving structure. These two systems work in tandem: AlphaTrail governs directional context, while MFI refines the timing.
FEATURES
Dynamic AlphaTrail line with regime switching logic that controls directional bias and bar coloring.
Smoothed MFI with gradient coloring to visually communicate pressure and exhaustion levels.
Overbought/oversold thresholds (80/20), mid-level (50), and custom extreme zones (90/10) for deeper signal granularity.
Built-in take-profit signal logic: crossover of MFI into overbought with bullish AlphaTrail, or into oversold with bearish AlphaTrail.
Visual fills between price and AlphaTrail for clearer confirmation during trend phases.
Alerts for regime shifts, MFI crossovers, trail interactions, and bar color regime changes.
USAGE
Add the indicator to any chart. Use the AlphaTrail plot to define trend context: bullish (trailing below price) or bearish (trailing above). MFI values give supporting confirmation—favor long setups when MFI is rising and above 50 in a bullish regime, and shorts when MFI is falling and below 50 in a bearish regime. The colored fills help visually track strength; sharp changes in MFI crossing 80/20 or 90/10 zones often precede pullbacks or reversals. Use the plotted circles as optional take-profit signals when MFI and trend are extended. Adjust AlphaTrail length/multiplier and MFI smoothing to better match the asset’s volatility profile.
Path of Least ResistancePath of Least Resistance (PLR)
Concept Overview
The Path of Least Resistance indicator identifies key zones on your chart that act like "muddy" or "sticky" areas where price tends to get bogged down, creating choppy and unpredictable price action. Between these zones lie the "empty spaces" - clear paths where price can move freely with momentum and direction.
The Analogy: Muddy Fields vs Open Roads
Think of your chart like a landscape:
🟫 ZONES (Muddy/Sticky Areas)
Fair Value Gaps (FVGs) from higher timeframes
Pivot wick zones from higher timeframe pivots
Areas where price gets "stuck" and churns
Like walking through thick mud - slow, choppy, unpredictable movement
Price action becomes erratic and difficult to trade
🟢 EMPTY SPACES (Open Roads)
The clear areas between zones
Where price can move freely with momentum
Like driving on an open highway - smooth, directional movement
The "Path of Least Resistance" for price movement
Trading Philosophy
AVOID Trading Within Zones:
Price action is typically choppy and unpredictable
Higher probability of false signals and whipsaws
Like trying to drive through mud - you'll get stuck
TRADE Through the Empty Spaces:
Look for moves that travel between zones
Price tends to move with momentum and direction
Higher probability setups with cleaner price action
Like taking the highway instead of back roads
Zone Types Detected
Fair Value Gaps (FVGs)
Imbalances from higher timeframe candles
Areas where price "owes" a return visit
Often act as magnets, creating choppy price action
Pivot Wick Zones
Upper and lower wicks from higher timeframe pivots
Rejection areas where price previously struggled
Often create resistance/support that leads to choppy movement
Color Coding System
The zones dynamically change color based on current price position:
🔴 RED ZONES : Price is below the zone (bearish context)
🟢 GREEN ZONES : Price is above the zone (bullish context)
🔘 GRAY ZONES : Price is within the zone (neutral/choppy area)
The "Mum Trades" Strategy
The best trades - what we call "Mum trades" (trades so obvious even your mum could spot them) - happen in the empty spaces between zones:
✅ High Probability Characteristics:
Clear directional movement between zones
Less noise and false signals
Higher momentum and follow-through
Cleaner technical patterns
❌ Avoid These Areas:
Trading within the muddy zones
Expecting clean moves through sticky areas
Fighting against the natural flow of price
Key Features
Auto Timeframe Detection : Automatically selects appropriate higher timeframe
Dynamic Zone Management : Overlapping zones are automatically cleaned up
Real-time Alerts : Get notified when price enters/exits zones
Visual Clarity : Clean zone display with extending boundaries
How to Use
Identify the Zones : Let the indicator mark the muddy areas
Find the Paths : Look for clear spaces between zones
Plan Your Trades : Target moves that travel through empty space
Avoid the Mud : Stay away from trading within the zones
Follow the Flow : Trade with the path of least resistance
Remember
Price, like water, always seeks the path of least resistance. By identifying where that path is clear (empty spaces) versus where it's obstructed (zones), you can align your trading with the natural flow of the market rather than fighting against it.
The goal is simple: Trade the highways, avoid the mud.
Linear Regression Forecast (ADX Adaptive)Linear Regression Forecast (ADX Adaptive)
This indicator is a dynamic price projection tool that combines multiple linear regression forecasts into a single, adaptive forecast curve. By integrating trend strength via the ADX and directional bias, it aims to visualize how price might evolve in different market environments—from strong trends to mean-reverting conditions.
Core Concept:
This tool builds forward price projections based on a blend of linear regression models with varying lookback lengths (from 2 up to a user-defined max). It then adjusts those projections using two key mechanisms:
ADX-Weighted Forecast Blending
In trending conditions (high ADX), the model follows the raw forecast direction. In ranging markets (low ADX), the forecast flips or reverts, biasing toward mean-reversion. A logistic transformation of directional bias, controlled by a steepness parameter, determines how aggressively this blending reacts to price behavior.
Volatility Scaling
The forecast’s magnitude is scaled based on ADX and directional conviction. When trends are unclear (low ADX or neutral bias), the projection range expands to reflect greater uncertainty and volatility.
How It Works:
Regression Curve Generation
For each regression length from 2 to maxLength, a forward projection is calculated using least-squares linear regression on the selected price source. These forecasts are extrapolated into the future.
Directional Bias Calculation
The forecasted points are analyzed to determine a normalized bias value in the range -1 to +1, where +1 means strongly bullish, -1 means strongly bearish, and 0 means neutral.
Logistic Bias Transformation
The raw bias is passed through a logistic sigmoid function, with a user-defined steepness. This creates a probability-like weight that favors either following or reversing the forecast depending on market context.
ADX-Based Weighting
ADX determines the weighting between trend-following and mean-reversion modes. Below ADX 20, the model favors mean-reversion. Above 25, it favors trend-following. Between 20 and 25, it linearly blends the two.
Blended Forecast Curve
Each forecast point is blended between trend-following and mean-reverting values, scaled for volatility.
What You See:
Forecast Lines: Projected future price paths drawn in green or red depending on direction.
Bias Plot: A separate plot showing post-blend directional bias as a percentage, where +100 is strongly bullish and -100 is strongly bearish.
Neutral Line: A dashed horizontal line at 0 percent bias to indicate neutrality.
User Inputs:
-Max Regression Length
-Price Source
-Line Width
-Bias Steepness
-ADX Length and Smoothing
Use Cases:
Visualize expected price direction under different trend conditions
Adjust trading behavior depending on trending vs ranging markets
Combine with other tools for deeper analysis
Important Notes:
This indicator is for visualization and analysis only. It does not provide buy or sell signals and should not be used in isolation. It makes assumptions based on historical price action and should be interpreted with market context.
Disguised Candles by The School of Dalal StreetDisguised Candles corrects one of the subtle visual distortions present in normal candlestick charts — the mismatch between the close of one candle and the open of the next.
On many instruments (especially at day/session breaks), the next candle’s open often jumps due to price gaps or data feed behavior. This can make reading the flow of price action harder than necessary.
Disguised Candles fixes this by plotting synthetic candles where the open of each candle is forced to match the close of the previous one — creating a visually continuous flow of price.
Real candles are made fully transparent, so only the "corrected" candles are visible.
This allows traders to:
Visualize price flow as a smooth path
Better spot true directional shifts and trends
Avoid distractions caused by technical gaps that are not meaningful to their strategy
🚀 Pure visual clarity. No noise from false opens.
How it works:
The open of each synthetic candle = close of previous real candle
High, Low, Close remain unchanged
Colors are based on Close vs Corrected Open
Real chart candles are hidden under a transparent overlay
Use this as a clean canvas for trend analysis or as a foundation for building new visual systems.
CDP - Counter-Directional-Pivot🎯 CDP - Counter-Directional-Pivot
📊 Overview
The Counter-Directional-Pivot (CDP) indicator calculates five critical price levels based on the previous day's OHLC data, specifically designed for multi-timeframe analysis. Unlike standard pivot points, CDP levels are calculated using a unique formula that identifies potential reversal zones where price action often changes direction.
⚡ What Makes This Script Original
This implementation solves several technical challenges that existing pivot indicators face:
🔄 Multi-Timeframe Consistency: Values remain identical across all timeframes (1m, 5m, 1h, daily) - a common problem with many pivot implementations
🔒 Intraday Stability: Uses advanced value-locking technology to prevent the "stepping" effect that occurs when pivot lines shift during the trading session
💪 Robust Data Handling: Optimized for both liquid and illiquid stocks with enhanced data synchronization
🧮 CDP Calculation Formula
The indicator calculates five key levels using the previous day's High (H), Low (L), and Close (C):
CDP = (H + L + C) ÷ 3 (Central Decision Point)
AH = 2×CDP + H – 2×L (Anchor High - Strong Resistance)
NH = 2×CDP – L (Near High - Moderate Resistance)
AL = 2×CDP – 2×H + L (Anchor Low - Strong Support)
NL = 2×CDP – H (Near Low - Moderate Support)
✨ Key Features
🎨 Visual Elements
📈 Five Distinct Price Levels: Each with customizable colors and line styles
🏷️ Smart Label System: Shows exact price values for each level
📋 Optional Value Table: Displays all levels in an organized table format
🎯 Clean Chart Display: Minimal visual clutter while maximizing information
⚙️ Technical Advantages
🔐 Session-Locked Values: Prices are locked at market open, preventing intraday shifts
🔄 Multi-Timeframe Sync: Perfect consistency between daily and intraday charts
✅ Data Validation: Built-in checks ensure reliable calculations
🚀 Performance Optimized: Efficient code structure for fast loading
💼 Trading Applications
🔄 Reversal Zones: AH and AL often act as strong turning points
💥 Breakout Confirmation: Price movement beyond these levels signals trend continuation
🛡️ Risk Management: Use levels for stop-loss and take-profit placement
🏗️ Market Structure: Understand daily ranges and potential price targets
📚 How to Use
🚀 Basic Setup
Add the indicator to your chart (works on any timeframe)
Customize colors for easy identification of support/resistance zones
Enable the value table for quick reference of exact price levels
📈 Trading Strategy Examples
🟢 Long Bias: Look for bounces at NL or AL levels
🔴 Short Bias: Watch for rejections at NH or AH levels
💥 Breakout Trading: Enter positions when price decisively breaks through anchor levels
↔️ Range Trading: Use CDP as the central reference point for range-bound markets
🎯 Advanced Strategy Combinations
RSI Integration for Enhanced Signals: 📊
📉 Oversold Bounces: Combine RSI below 30 with price touching AL/NL levels for high-probability long entries
📈 Overbought Rejections: Look for RSI above 70 with price rejecting AH/NH levels for short opportunities
🔍 Divergence Confirmation: When RSI shows bullish divergence at support levels (AL/NL) or bearish divergence at resistance levels (AH/NH), it often signals stronger reversal potential
⚡ Momentum Confluence: RSI crossing 50 while price breaks through CDP can confirm trend direction changes
⚙️ Configuration Options
🎨 Line Customization: Adjust width, style (solid/dashed/dotted), and colors
👁️ Display Preferences: Toggle individual levels, labels, and value table
📍 Table Position: Place the value table anywhere on your chart
🔔 Alert System: Get notifications when price crosses key levels
🔧 Technical Implementation Details
🎯 Data Reliability
The script uses request.security() with lookahead settings to ensure historical accuracy while maintaining real-time functionality. The value-locking mechanism prevents the common issue where pivot levels shift during the trading day.
🔄 Multi-Timeframe Logic
⏰ Intraday Charts: Display previous day's calculated levels as stable horizontal lines
📅 Daily Charts: Show current day's levels based on yesterday's OHLC
🔍 Consistency Check: All timeframes reference the same source data
🤔 Why CDP vs Standard Pivots?
Counter-Directional Pivots often provide more accurate reversal points than traditional pivot calculations because they incorporate the relationship between high/low ranges and closing prices more effectively. The formula creates levels that better reflect market psychology and institutional trading behaviors.
💡 Best Practices
💧 Use on liquid markets for most reliable results
📊 RSI Combination: Add RSI indicator for overbought/oversold confirmation and divergence analysis
📊 Combine with volume analysis for confirmation
🔍 Consider multiple timeframe analysis (daily levels on hourly charts)
📝 Test thoroughly in paper trading before live implementation
💪 Example Market Applications
NASDAQ:AAPL AAPL - Tech stock breakouts through AH levels
$NYSE:SPY SPY - Index trading with CDP range analysis
NASDAQ:TSLA TSLA - Volatile stock reversals at AL/NL levels
⚠️ This indicator is designed for educational and analytical purposes. Always combine with proper risk management and additional technical analysis tools.
StatMetricsLibrary "StatMetrics"
A utility library for common statistical indicators and ratios used in technical analysis.
Includes Z-Score, correlation, PLF, SRI, Sharpe, Sortino, Omega ratios, and normalization tools.
zscore(src, len)
Calculates the Z-score of a series
Parameters:
src (float) : The input price or series (e.g., close)
len (simple int) : The lookback period for mean and standard deviation
Returns: Z-score: number of standard deviations the input is from the mean
corr(x, y, len)
Computes Pearson correlation coefficient between two series
Parameters:
x (float) : First series
y (float) : Second series
len (simple int) : Lookback period
Returns: Correlation coefficient between -1 and 1
plf(src, longLen, shortLen, smoothLen)
Calculates the Price Lag Factor (PLF) as the difference between long and short Z-scores, normalized and smoothed
Parameters:
src (float) : Source series (e.g., close)
longLen (simple int) : Long Z-score period
shortLen (simple int) : Short Z-score period
smoothLen (simple int) : Hull MA smoothing length
Returns: Smoothed and normalized PLF oscillator
sri(signal, len)
Computes the Statistical Reliability Index (SRI) based on trend persistence
Parameters:
signal (float) : A price or signal series (e.g., smoothed PLF)
len (simple int) : Lookback period for smoothing and deviation
Returns: Normalized trend reliability score
sharpe(src, len)
Calculates the Sharpe Ratio over a period
Parameters:
src (float) : Price series (e.g., close)
len (simple int) : Lookback period
Returns: Sharpe ratio value
sortino(src, len)
Calculates the Sortino Ratio over a period, using only downside volatility
Parameters:
src (float) : Price series
len (simple int) : Lookback period
Returns: Sortino ratio value
omega(src, len)
Calculates the Omega Ratio as the ratio of upside to downside return area
Parameters:
src (float) : Price series
len (simple int) : Lookback period
Returns: Omega ratio value
beta(asset, benchmark, len)
Calculates beta coefficient of asset vs benchmark using rolling covariance
Parameters:
asset (float) : Series of the asset (e.g., close)
benchmark (float) : Series of the benchmark (e.g., SPX close)
len (simple int) : Lookback window
Returns: Beta value (slope of linear regression)
alpha(asset, benchmark, len)
Calculates rolling alpha of an asset relative to a benchmark
Parameters:
asset (float) : Series of the asset (e.g., close)
benchmark (float) : Series of the benchmark (e.g., SPX close)
len (simple int) : Lookback window
Returns: Alpha value (excess return not explained by Beta exposure)
skew(x, len)
Computes skewness of a return series
Parameters:
x (float) : Input series (e.g., returns)
len (simple int) : Lookback period
Returns: Skewness value
kurtosis(x, len)
Computes kurtosis of a return series
Parameters:
x (float) : Input series (e.g., returns)
len (simple int) : Lookback period
Returns: Kurtosis value
cv(x, len)
Calculates Coefficient of Variation
Parameters:
x (float) : Input series (e.g., returns or prices)
len (simple int) : Lookback period
Returns: CV value
autocorr(x, len)
Calculates autocorrelation with 1-lag
Parameters:
x (float) : Series to test
len (simple int) : Lookback window
Returns: Autocorrelation at lag 1
stderr(x, len)
Calculates rolling standard error of a series
Parameters:
x (float) : Input series
len (simple int) : Lookback window
Returns: Standard error (std dev / sqrt(n))
info_ratio(asset, benchmark, len)
Calculates the Information Ratio
Parameters:
asset (float) : Asset price series
benchmark (float) : Benchmark price series
len (simple int) : Lookback period
Returns: Information ratio (alpha / tracking error)
tracking_error(asset, benchmark, len)
Measures deviation from benchmark (Tracking Error)
Parameters:
asset (float) : Asset return series
benchmark (float) : Benchmark return series
len (simple int) : Lookback window
Returns: Tracking error value
max_drawdown(x, len)
Computes maximum drawdown over a rolling window
Parameters:
x (float) : Price series
len (simple int) : Lookback window
Returns: Rolling max drawdown percentage (as a negative value)
zscore_signal(z, ob, os)
Converts Z-score into a 3-level signal
Parameters:
z (float) : Z-score series
ob (float) : Overbought threshold
os (float) : Oversold threshold
Returns: -1, 0, or 1 depending on signal state
r_squared(x, y, len)
Calculates rolling R-squared (coefficient of determination)
Parameters:
x (float) : Asset returns
y (float) : Benchmark returns
len (simple int) : Lookback window
Returns: R-squared value (0 to 1)
entropy(x, len)
Approximates Shannon entropy using log returns
Parameters:
x (float) : Price series
len (simple int) : Lookback period
Returns: Approximate entropy
zreversal(z)
Detects Z-score reversals to the mean
Parameters:
z (float) : Z-score series
Returns: +1 on upward reversal, -1 on downward
momentum_rank(x, len)
Calculates relative momentum strength
Parameters:
x (float) : Price series
len (simple int) : Lookback window
Returns: Proportion of lookback where current price is higher
normalize(x, len)
Normalizes a series to a 0–1 range over a period
Parameters:
x (float) : The input series
len (simple int) : Lookback period
Returns: Normalized value between 0 and 1
composite_score(score1, score2, score3)
Combines multiple normalized scores into a composite score
Parameters:
score1 (float)
score2 (float)
score3 (float)
Returns: Average composite score
GStrategy 1000Pepe 15mTrend Following Candlestick Strategy with EMA Filter and Exit Delay
Strategy Concept
This strategy combines candlestick patterns with EMA trend filtering to identify high-probability trade entries, featuring:
Entry Signals: Hammer and Engulfing patterns confirmed by EMA trend
Trend Filter: Fast EMA (20) vs Slow EMA (50) crossover system
Risk Management: 5% stop-loss + 1% trailing stop
Smart Exit: 2-bar delay after exit signals to avoid whipsaws
Key Components
Trend Identification:
Uptrend: Fast EMA > Slow EMA AND rising
Downtrend: Fast EMA < Slow EMA AND falling
Entry Conditions:
pinescript
// Bullish Entry (Long)
longCondition = (Hammer OR Bullish Engulfing)
AND Uptrend
AND no existing position
// Bearish Entry (Short)
shortCondition = Bearish Engulfing
AND Downtrend
AND no existing position
Exit Mechanics:
Primary Exit: EMA crossover (Fast crosses Slow)
Delayed Execution: Waits 2 full candles after signal
Emergency Exits:
5% fixed stop-loss
1% trailing stop
Visual Dashboard:
Colored EMA lines (Blue=Fast, Red=Slow)
Annotated candlestick patterns
Background highlighting for signals
Distinct markers for entries/exits
Unique Features
Pattern Recognition:
Enhanced Hammer detection (strict body/wick ratios)
Multi-candle engulfing confirmation
Trend-Confirmation:
Requires price and EMA alignment
Filters counter-trend patterns
Exit Optimization:
pinescript
// Delay implementation
if exit_signal_triggered
counter := 2 // Start countdown
else if counter > 0
counter -= 1 // Decrement each bar
exit_trade = (counter == 1) // Execute on final bar
Risk Parameters
Parameter Value Description
Stop Loss 5% Fixed risk per trade
Trailing Stop 1% Locks in profits
Exit Delay 2 bars Reduces false exits
Position Size 100% No pyramiding
Visualization Examples
🟢 Green Triangle: Bullish entry
🔴 Red Triangle: Bearish entry
⬇️ Blue X: Long exit (after delay)
⬆️ Green X: Short exit (after delay)
🎯 Pattern Labels: Identifies hammer/engulfing
Recommended Use
Timeframes: 1H-4H (reduces noise)
Markets: Trend-prone assets (FX, indices)
Best Conditions: Strong trending markets
Avoid: Choppy/Ranging markets
Candle Range % vs 8-Candle AvgCandle % Indicator – Measure Candle Strength by Range %
**Overview:**
The *Candle % Indicator* helps traders visually and analytically gauge the strength or significance of a price candle relative to its recent historical context. This is particularly useful for detecting breakout moves, volatility shifts, or overextended candles that may signal exhaustion.
**What It Does:**
* Calculates the **percentage range** of the current candle compared to the **average range of the past N candles**.
* Highlights candles that exceed a user-defined threshold (e.g., 150% of the average range).
* Useful for **filtering out extreme candles** that might represent anomalies or unsustainable moves.
* Can be combined with other strategies (like EMA crossovers, support/resistance breaks, etc.) to improve signal quality.
**Use Case Examples:**
***Filter out fakeouts** in breakout strategies by ignoring candles that are overly large and may revert.
***Volatility control**: Avoid entries when market conditions are erratic.
**Confluence**: Combine with EMA or RSI signals for refined entries.
**How to Read:**
* If a candle is larger than the average range by more than the set percentage (default 150%), it's flagged (e.g., no entry signal or optional visual marker).
* Ideal for intraday, swing, or algorithmic trading setups.
**Customizable Inputs:**
**Lookback Period**: Number of previous candles to calculate the average range.
**% Threshold**: Maximum percentage a candle can exceed the average before being filtered or marked.
Liquidity Engulfing (Nephew_Sam_)🔥 Liquidity Engulfing Multi-Timeframe Detector
This indicator finds engulfing bars which have swept liquidity from its previous candle. You can use it across 6 timeframes with fibonacci entries.
⚡ Key Features
6 Customizable Timeframes - Complete market structure analysis
Smart Liquidity Detection - Finds patterns that sweep liquidity then reverse
Real-Time Status Table - Confirmed vs unconfirmed patterns with color coding
Fibonacci Integration - 5 customizable fib levels for precise entries
HTF → LTF Strategy - Spot reversals on higher timeframes, enter on lower timeframe fibs
📈 Engulfing Rules
Bullish: Current candle bullish + previous bearish + current low < previous low + current close > previous open
Bearish: Current candle bearish + previous bullish + current high > previous high + current close < previous open
Langlands-Operadic Möbius Vortex (LOMV)Langlands-Operadic Möbius Vortex (LOMV)
Where Pure Mathematics Meets Market Reality
A Revolutionary Synthesis of Number Theory, Category Theory, and Market Dynamics
🎓 THEORETICAL FOUNDATION
The Langlands-Operadic Möbius Vortex represents a groundbreaking fusion of three profound mathematical frameworks that have never before been combined for market analysis:
The Langlands Program: Harmonic Analysis in Markets
Developed by Robert Langlands (Fields Medal recipient), the Langlands Program creates bridges between number theory, algebraic geometry, and harmonic analysis. In our indicator:
L-Function Implementation:
- Utilizes the Möbius function μ(n) for weighted price analysis
- Applies Riemann zeta function convergence principles
- Calculates quantum harmonic resonance between -2 and +2
- Measures deep mathematical patterns invisible to traditional analysis
The L-Function core calculation employs:
L_sum = Σ(return_val × μ(n) × n^(-s))
Where s is the critical strip parameter (0.5-2.5), controlling mathematical precision and signal smoothness.
Operadic Composition Theory: Multi-Strategy Democracy
Category theory and operads provide the mathematical framework for composing multiple trading strategies into a unified signal. This isn't simple averaging - it's mathematical composition using:
Strategy Composition Arity (2-5 strategies):
- Momentum analysis via RSI transformation
- Mean reversion through Bollinger Band mathematics
- Order Flow Polarity Index (revolutionary T3-smoothed volume analysis)
- Trend detection using Directional Movement
- Higher timeframe momentum confirmation
Agreement Threshold System: Democratic voting where strategies must reach consensus before signal generation. This prevents false signals during market uncertainty.
Möbius Function: Number Theory in Action
The Möbius function μ(n) forms the mathematical backbone:
- μ(n) = 1 if n is a square-free positive integer with even number of prime factors
- μ(n) = -1 if n is a square-free positive integer with odd number of prime factors
- μ(n) = 0 if n has a squared prime factor
This creates oscillating weights that reveal hidden market periodicities and harmonic structures.
🔧 COMPREHENSIVE INPUT SYSTEM
Langlands Program Parameters
Modular Level N (5-50, default 30):
Primary lookback for quantum harmonic analysis. Optimized by timeframe:
- Scalping (1-5min): 15-25
- Day Trading (15min-1H): 25-35
- Swing Trading (4H-1D): 35-50
- Asset-specific: Crypto 15-25, Stocks 30-40, Forex 35-45
L-Function Critical Strip (0.5-2.5, default 1.5):
Controls Riemann zeta convergence precision:
- Higher values: More stable, smoother signals
- Lower values: More reactive, catches quick moves
- High frequency: 0.8-1.2, Medium: 1.3-1.7, Low: 1.8-2.3
Frobenius Trace Period (5-50, default 21):
Galois representation lookback for price-volume correlation:
- Measures harmonic relationships in market flows
- Scalping: 8-15, Day Trading: 18-25, Swing: 25-40
HTF Multi-Scale Analysis:
Higher timeframe context prevents trading against major trends:
- Provides market bias and filters signals
- Improves win rates by 15-25% through trend alignment
Operadic Composition Parameters
Strategy Composition Arity (2-5, default 4):
Number of algorithms composed for final signal:
- Conservative: 4-5 strategies (higher confidence)
- Moderate: 3-4 strategies (balanced approach)
- Aggressive: 2-3 strategies (more frequent signals)
Category Agreement Threshold (2-5, default 3):
Democratic voting minimum for signal generation:
- Higher agreement: Fewer but higher quality signals
- Lower agreement: More signals, potential false positives
Swiss-Cheese Mixing (0.1-0.5, default 0.382):
Golden ratio φ⁻¹ based blending of trend factors:
- 0.382 is φ⁻¹, optimal for natural market fractals
- Higher values: Stronger trend following
- Lower values: More contrarian signals
OFPI Configuration:
- OFPI Length (5-30, default 14): Order Flow calculation period
- T3 Smoothing (3-10, default 5): Advanced exponential smoothing
- T3 Volume Factor (0.5-1.0, default 0.7): Smoothing aggressiveness control
Unified Scoring System
Component Weights (sum ≈ 1.0):
- L-Function Weight (0.1-0.5, default 0.3): Mathematical harmony emphasis
- Galois Rank Weight (0.1-0.5, default 0.2): Market structure complexity
- Operadic Weight (0.1-0.5, default 0.3): Multi-strategy consensus
- Correspondence Weight (0.1-0.5, default 0.2): Theory-practice alignment
Signal Threshold (0.5-10.0, default 5.0):
Quality filter producing:
- 8.0+: EXCEPTIONAL signals only
- 6.0-7.9: STRONG signals
- 4.0-5.9: MODERATE signals
- 2.0-3.9: WEAK signals
🎨 ADVANCED VISUAL SYSTEM
Multi-Dimensional Quantum Aura Bands
Five-layer resonance field showing market energy:
- Colors: Theme-matched gradients (Quantum purple, Holographic cyan, etc.)
- Expansion: Dynamic based on score intensity and volatility
- Function: Multi-timeframe support/resistance zones
Morphism Flow Portals
Category theory visualization showing market topology:
- Green/Cyan Portals: Bullish mathematical flow
- Red/Orange Portals: Bearish mathematical flow
- Size/Intensity: Proportional to signal strength
- Recursion Depth (1-8): Nested patterns for flow evolution
Fractal Grid System
Dynamic support/resistance with projected L-Scores:
- Multiple Timeframes: 10, 20, 30, 40, 50-period highs/lows
- Smart Spacing: Prevents level overlap using ATR-based minimum distance
- Projections: Estimated signal scores when price reaches levels
- Usage: Precise entry/exit timing with mathematical confirmation
Wick Pressure Analysis
Rejection level prediction using candle mathematics:
- Upper Wicks: Selling pressure zones (purple/red lines)
- Lower Wicks: Buying pressure zones (purple/green lines)
- Glow Intensity (1-8): Visual emphasis and line reach
- Application: Confluence with fractal grid creates high-probability zones
Regime Intensity Heatmap
Background coloring showing market energy:
- Black/Dark: Low activity, range-bound markets
- Purple Glow: Building momentum and trend development
- Bright Purple: High activity, strong directional moves
- Calculation: Combines trend, momentum, volatility, and score intensity
Six Professional Themes
- Quantum: Purple/violet for general trading and mathematical focus
- Holographic: Cyan/magenta optimized for cryptocurrency markets
- Crystalline: Blue/turquoise for conservative, stability-focused trading
- Plasma: Gold/magenta for high-energy volatility trading
- Cosmic Neon: Bright neon colors for maximum visibility and aggressive trading
📊 INSTITUTIONAL-GRADE DASHBOARD
Unified AI Score Section
- Total Score (-10 to +10): Primary decision metric
- >5: Strong bullish signals
- <-5: Strong bearish signals
- Quality ratings: EXCEPTIONAL > STRONG > MODERATE > WEAK
- Component Analysis: Individual L-Function, Galois, Operadic, and Correspondence contributions
Order Flow Analysis
Revolutionary OFPI integration:
- OFPI Value (-100% to +100%): Real buying vs selling pressure
- Visual Gauge: Horizontal bar chart showing flow intensity
- Momentum Status: SHIFTING, ACCELERATING, STRONG, MODERATE, or WEAK
- Trading Application: Flow shifts often precede major moves
Signal Performance Tracking
- Win Rate Monitoring: Real-time success percentage with emoji indicators
- Signal Count: Total signals generated for frequency analysis
- Current Position: LONG, SHORT, or NONE with P&L tracking
- Volatility Regime: HIGH, MEDIUM, or LOW classification
Market Structure Analysis
- Möbius Field Strength: Mathematical field oscillation intensity
- CHAOTIC: High complexity, use wider stops
- STRONG: Active field, normal position sizing
- MODERATE: Balanced conditions
- WEAK: Low activity, consider smaller positions
- HTF Trend: Higher timeframe bias (BULL/BEAR/NEUTRAL)
- Strategy Agreement: Multi-algorithm consensus level
Position Management
When in trades, displays:
- Entry Price: Original signal price
- Current P&L: Real-time percentage with risk level assessment
- Duration: Bars in trade for timing analysis
- Risk Level: HIGH/MEDIUM/LOW based on current exposure
🚀 SIGNAL GENERATION LOGIC
Balanced Long/Short Architecture
The indicator generates signals through multiple convergent pathways:
Long Entry Conditions:
- Score threshold breach with algorithmic agreement
- Strong bullish order flow (OFPI > 0.15) with positive composite signal
- Bullish pattern recognition with mathematical confirmation
- HTF trend alignment with momentum shifting
- Extreme bullish OFPI (>0.3) with any positive score
Short Entry Conditions:
- Score threshold breach with bearish agreement
- Strong bearish order flow (OFPI < -0.15) with negative composite signal
- Bearish pattern recognition with mathematical confirmation
- HTF trend alignment with momentum shifting
- Extreme bearish OFPI (<-0.3) with any negative score
Exit Logic:
- Score deterioration below continuation threshold
- Signal quality degradation
- Opposing order flow acceleration
- 10-bar minimum between signals prevents overtrading
⚙️ OPTIMIZATION GUIDELINES
Asset-Specific Settings
Cryptocurrency Trading:
- Modular Level: 15-25 (capture volatility)
- L-Function Precision: 0.8-1.3 (reactive to price swings)
- OFPI Length: 10-20 (fast correlation shifts)
- Cascade Levels: 5-7, Theme: Holographic
Stock Index Trading:
- Modular Level: 25-35 (balanced trending)
- L-Function Precision: 1.5-1.8 (stable patterns)
- OFPI Length: 14-20 (standard correlation)
- Cascade Levels: 4-5, Theme: Quantum
Forex Trading:
- Modular Level: 35-45 (smooth trends)
- L-Function Precision: 1.6-2.1 (high smoothing)
- OFPI Length: 18-25 (disable volume amplification)
- Cascade Levels: 3-4, Theme: Crystalline
Timeframe Optimization
Scalping (1-5 minute charts):
- Reduce all lookback parameters by 30-40%
- Increase L-Function precision for noise reduction
- Enable all visual elements for maximum information
- Use Small dashboard to save screen space
Day Trading (15 minute - 1 hour):
- Use default parameters as starting point
- Adjust based on market volatility
- Normal dashboard provides optimal information density
- Focus on OFPI momentum shifts for entries
Swing Trading (4 hour - Daily):
- Increase lookback parameters by 30-50%
- Higher L-Function precision for stability
- Large dashboard for comprehensive analysis
- Emphasize HTF trend alignment
🏆 ADVANCED TRADING STRATEGIES
The Mathematical Confluence Method
1. Wait for Fractal Grid level approach
2. Confirm with projected L-Score > threshold
3. Verify OFPI alignment with direction
4. Enter on portal signal with quality ≥ STRONG
5. Exit on score deterioration or opposing flow
The Regime Trading System
1. Monitor Aether Flow background intensity
2. Trade aggressively during bright purple periods
3. Reduce position size during dark periods
4. Use Möbius Field strength for stop placement
5. Align with HTF trend for maximum probability
The OFPI Momentum Strategy
1. Watch for momentum shifting detection
2. Confirm with accelerating flow in direction
3. Enter on immediate portal signal
4. Scale out at Fibonacci levels
5. Exit on flow deceleration or reversal
⚠️ RISK MANAGEMENT INTEGRATION
Mathematical Position Sizing
- Use Galois Rank for volatility-adjusted sizing
- Möbius Field strength determines stop width
- Fractal Dimension guides maximum exposure
- OFPI momentum affects entry timing
Signal Quality Filtering
- Trade only STRONG or EXCEPTIONAL quality signals
- Increase position size with higher agreement levels
- Reduce risk during CHAOTIC Möbius field periods
- Respect HTF trend alignment for directional bias
🔬 DEVELOPMENT JOURNEY
Creating the LOMV was an extraordinary mathematical undertaking that pushed the boundaries of what's possible in technical analysis. This indicator almost didn't happen. The theoretical complexity nearly proved insurmountable.
The Mathematical Challenge
Implementing the Langlands Program required deep research into:
- Number theory and the Möbius function
- Riemann zeta function convergence properties
- L-function analytical continuation
- Galois representations in finite fields
The mathematical literature spans decades of pure mathematics research, requiring translation from abstract theory to practical market application.
The Computational Complexity
Operadic composition theory demanded:
- Category theory implementation in Pine Script
- Multi-dimensional array management for strategy composition
- Real-time democratic voting algorithms
- Performance optimization for complex calculations
The Integration Breakthrough
Bringing together three disparate mathematical frameworks required:
- Novel approaches to signal weighting and combination
- Revolutionary Order Flow Polarity Index development
- Advanced T3 smoothing implementation
- Balanced signal generation preventing directional bias
Months of intensive research culminated in breakthrough moments when the mathematics finally aligned with market reality. The result is an indicator that reveals market structure invisible to conventional analysis while maintaining practical trading utility.
🎯 PRACTICAL IMPLEMENTATION
Getting Started
1. Apply indicator with default settings
2. Select appropriate theme for your markets
3. Observe dashboard metrics during different market conditions
4. Practice signal identification without trading
5. Gradually adjust parameters based on observations
Signal Confirmation Process
- Never trade on score alone - verify quality rating
- Confirm OFPI alignment with intended direction
- Check fractal grid level proximity for timing
- Ensure Möbius field strength supports position size
- Validate against HTF trend for bias confirmation
Performance Monitoring
- Track win rate in dashboard for strategy assessment
- Monitor component contributions for optimization
- Adjust threshold based on desired signal frequency
- Document performance across different market regimes
🌟 UNIQUE INNOVATIONS
1. First Integration of Langlands Program mathematics with practical trading
2. Revolutionary OFPI with T3 smoothing and momentum detection
3. Operadic Composition using category theory for signal democracy
4. Dynamic Fractal Grid with projected L-Score calculations
5. Multi-Dimensional Visualization through morphism flow portals
6. Regime-Adaptive Background showing market energy intensity
7. Balanced Signal Generation preventing directional bias
8. Professional Dashboard with institutional-grade metrics
📚 EDUCATIONAL VALUE
The LOMV serves as both a practical trading tool and an educational gateway to advanced mathematics. Traders gain exposure to:
- Pure mathematics applications in markets
- Category theory and operadic composition
- Number theory through Möbius function implementation
- Harmonic analysis via L-function calculations
- Advanced signal processing through T3 smoothing
⚖️ RESPONSIBLE USAGE
This indicator represents advanced mathematical research applied to market analysis. While the underlying mathematics are rigorously implemented, markets remain inherently unpredictable.
Key Principles:
- Use as part of comprehensive trading strategy
- Implement proper risk management at all times
- Backtest thoroughly before live implementation
- Understand that past performance does not guarantee future results
- Never risk more than you can afford to lose
The mathematics reveal deep market structure, but successful trading requires discipline, patience, and sound risk management beyond any indicator.
🔮 CONCLUSION
The Langlands-Operadic Möbius Vortex represents a quantum leap forward in technical analysis, bringing PhD-level pure mathematics to practical trading while maintaining visual elegance and usability.
From the harmonic analysis of the Langlands Program to the democratic composition of operadic theory, from the number-theoretic precision of the Möbius function to the revolutionary Order Flow Polarity Index, every component works in mathematical harmony to reveal the hidden order within market chaos.
This is more than an indicator - it's a mathematical lens that transforms how you see and understand market structure.
Trade with mathematical precision. Trade with the LOMV.
*"Mathematics is the language with which God has written the universe." - Galileo Galilei*
*In markets, as in nature, profound mathematical beauty underlies apparent chaos. The LOMV reveals this hidden order.*
— Dskyz, Trade with insight. Trade with anticipation.
Fallback VWAP (No Volume? No Problem!) – Yogi365Fallback VWAP (No Volume? No Problem!) – Yogi365
This script plots Daily, Weekly, and Monthly VWAPs with ±1 Standard Deviation bands. When volume data is missing or zero (common in indices or illiquid assets), it automatically falls back to a TWAP-style calculation, ensuring that your VWAP levels always remain visible and accurate.
Features:
Daily, Weekly, and Monthly VWAPs with ±1 Std Dev bands.
Auto-detection of missing volume and seamless fallback.
Clean, color-coded trend table showing price vs VWAP/bands.
Uses hlc3 for VWAP source.
Labels indicate when fallback is used.
Best Used On:
Any asset or index where volume is unavailable.
Intraday and swing trading.
Works on all timeframes but optimized for overlay use.
How it Works:
If volume == 0, the script uses a constant fallback volume (1), turning the VWAP into a TWAP (Time-Weighted Average Price) — still useful for intraday or index-based analysis.
This ensures consistent plotting on instruments like indices (e.g., NIFTY, SENSEX,DJI etc.) which might not provide volume on TradingView.
Trend Persistence Counter (TPC) by riskcipher🧭 Trend Persistence Counter (TPC) – A Simple Price Action Trend Duration Tool
Trend Persistence Counter (TPC) is a lightweight indicator that counts how long a trend persists after a breakout.
It is entirely based on price action, without using any moving averages or smoothing. The goal is to give a simple, rule-based view of trend continuity.
🧠 How It Works (Logic Overview)
This indicator switches between two modes: bullish and bearish.
If close > previous high, the counter enters bullish mode, and starts at +1
While in bullish mode:
If close >= previous low → continue the uptrend → +1 each bar
If close < previous low → trend ends → reset to 0, switch to bearish mode
If close < previous low, the counter enters bearish mode, and starts at -1
While in bearish mode:
If close <= previous high → continue the downtrend → -1 each bar
If close > previous high → trend ends → reset to 0, switch to bullish mode
This provides a bar-by-bar count of trend persistence based on whether price holds structure.
🎯 Use Cases
Track how long a trend continues after a breakout
Quickly detect when trend structure breaks
Help visually filter “strong” vs “weak” moves
Build logic-based alerts (e.g., trend continues for N bars)
🔍 Why Use This Instead of Traditional Indicators?
This is not meant to replace moving averages or trend filters.
But it offers some advantages for those who prefer structure-based logic:
Feature TPC
Based on Price Action ✅ Yes
Uses Lagging Filters ❌ No moving average or smoothing
Trend Duration Measurement ✅ Counts valid consecutive moves
Complexity ⚪ Very simple and transparent
It’s a simple concept and easy to understand, but still useful when combined with other tools or visualized on its own.
⚙️ Technical Notes
Works on any timeframe or instrument
The value is positive during bullish persistence, negative during bearish
Value resets to 0 when trend structure breaks
All logic is calculated bar-by-bar, in real time
✅ Example Usage Ideas
Highlight candles when TPC value crosses a certain threshold (e.g., strong breakout continuation)
Use the zero-cross as a potential reversal warning
Filter trend signals in your existing strategies
AWR Optimized LR GraphHello Trading Viewers !
Drawing linear regression channels at the best place and for many periods can be time consuming.
In the library, I've found some indicators that draw 1 or 2 but based on fixed number of bars or a duration...
Not always relevant, that's why I decide to create this indicator.
It calculates 8 linear regression channels according to 8 differents configurable periods.
Each time, the indicator will calculate for each specified duration range the best linear regression line & channel (2 standard regressions) for that period and then plot it on the graph.
You can settle how many linear regression channels you want to display.
For period, defaults configurations (number of candles studied) are :
Period 1
min1 = 33
max1 = 66
Period 2
min2 = 67
max2 = 128
Period 3
min3 = 129
max3 = 255
Period 4
min4 = 256
max4 = 510
Period 5
min5 = 511
max5 = 1020
Period 6
min6 = 1021
max6 = 2040
Period 7
min7 = 2041
max7 = 3500
Period 8
min8 = 3501
max8 = 4999
This default settings provide short-term, mid term, long term and a very long-term view.
You have to go back on the chart to display the channels that start on previous period that are currently not on the screen.
You can set a specific color for each linear regression channels.
The linear regression line is based on the least squares method, meaning: it calculates along each period the gap between a linear & the price & squarred it. Then it defines the linear in order to have always the least distance between price and the linear.
The more the price deviates from its regression line, the more statistically likely it is to return to its regression line.
Application of Regression Lines in Trading
Regression lines are widely used in trading and financial analysis to understand market trends and make informed predictions. Here are some key applications:
1. Trend Identification – Traders use regression lines to visualize the general direction of a stock or asset price, helping to confirm an upward or downward trend.
2. Price Predictions – Linear regression models assist in estimating future price movements based on historical data, allowing traders to anticipate changes.
3. Risk Assessment – By analyzing the slope and variation of a regression line, traders can gauge market volatility and potential risks.
4. Support and Resistance Levels – Regression channels help traders identify support and resistance zones, providing insight into optimal entry and exit points in a trend.
5. You can also use the short period linear regression channels vs the long period linear regression channels to identify important pivot points.
Combined ATPC & MACD DivergenceTrend Optimizer + Divergence Finder in One Unified Tool
🔍 Overview:
This powerful dual-system indicator merges two proven analytical engines:
✅ The Algorganic Typical Price Channel (ATPC) — a custom trend oscillator that highlights mean-reversion and directional bias.
✅ A refined MACD system with divergence detection, enhanced with an adjusted Donchian midline for real-time trend strength filtering.
Together, they provide a high-confidence, multi-signal system ideal for swing trading, scalping, or confirming reversals with context.
⚙️ Core Components & Logic
🧠 1. ATPC Engine (Trend Commodity Index)
A momentum and volatility-normalized oscillator based on the typical price (H+L+C)/3:
TrendCI Line (Blue) – Main trend signal based on smoothed CCI logic.
TrendLine2 (Orange) – A slower smoothing of TrendCI for crossovers.
Key Zones (customizable):
🔴 Ultra Overbought: +73
🟣 Overbought: +58
🟣 Oversold: -58
🔴 Ultra Oversold: -73
Trade Logic:
✅ Buy Signal: TrendCI crosses above TrendLine2 while in oversold zone
❌ Sell Signal: TrendCI crosses below TrendLine2 while in overbought zone
Additional visual feedback:
Histogram Bars show strength and direction of momentum shift
Green/Red Circles highlight potential long/short setups
📉 2. MACD System + Divergence Finder
Classic MACD enhanced with a Donchian Midline overlay to filter trend bias.
🔷 MACD Line and 🟠 Signal Line show crossover momentum
🟩/🟥 Histogram shows distance from the signal line
🟪 Adjusted Donchian Midline dynamically adapts to range-bound vs trending environments
Background Color provides real-time trend state:
✅ Green = Bullish Trend
❌ Red = Bearish Trend
No color = Neutral / Choppy
MACD Boundaries (user-defined):
Overbought: +1.0
Oversold: -1.0
🔀 3. Divergence Detection
Spot hidden power shifts before price reacts:
🔼 Positive Divergence – Price makes lower lows, but MACD histogram rises
🔽 Negative Divergence – Price makes higher highs, but MACD histogram weakens
These are visually marked with:
Green “+Div” label (bullish reversal cue)
Red “–Div” label (bearish exhaustion signal)
🎯 How to Use It
For Trend Traders:
Stay in sync with macro trend using MACD histogram + background
Use ATPC crossovers for precision entries
Avoid signals during neutral background (chop filter)
For Reversal Traders:
Look for bullish +Div with ATPC buy signal in oversold zone
Look for bearish –Div with ATPC sell signal in overbought zone
Mid-Donchian line can act as confluence or breakout trigger
For Scalpers & Intraday Traders:
Combine with VWAP, liquidity zones, or order flow levels
ATPC crossovers + MACD histogram zero-line flip = potential scalp entry
Use histogram slope and divergence to avoid false momentum traps
🧩 Customizable Inputs
🎛️ ATPC: Channel & Smoothing lengths, overbought/oversold thresholds
🎛️ MACD: Fast/slow EMAs, signal smoothing, Donchian period, bounds
🎨 Fully theme-compatible with adjustable colors and line styles
🔔 Alerts (Add Your Own)
While this version doesn’t contain built-in alerts, you can easily add alerts based on:
buySignal or sellSignal from ATPC logic
Histogram cross zero or trend flip
MACD Divergence event
📜 “This indicator doesn't just show signals—it tells a story about who’s in control of the market, and when that control might be slipping.”
Not-So-Average True Range (nsATR)Not-So-Average True Range (nsATR)
*By Sherlock_MacGyver*
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Long Story Short
The nsATR is a complete overhaul of traditional ATR analysis. It was designed to solve the fundamental issues with standard ATR, such as lag, lack of contextual awareness, and equal treatment of all volatility events.
Key innovations include:
* A smarter ATR that reacts dynamically when price movement exceeds normal expectations.
* Envelope zones that distinguish between moderate and extreme volatility conditions.
* A long-term ATR baseline that adds historical context to current readings.
* A compression detection system that flags when the market is coiled and ready to break out.
This indicator is designed for traders who want to see volatility the way it actually behaves — contextually, asymmetrically, and with predictive power.
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What Is This Thing?
Standard ATR (Average True Range) has limitations:
* It smooths too slowly (using Wilder's RMA), which delays detection of meaningful moves.
* It lacks context — no way to know if current volatility is high or low relative to history.
* It treats all volatility equally, regardless of scale or significance.
nsATR** was built from scratch to overcome these weaknesses by applying:
* Amplification of large True Range spikes.
* Visual envelope zones for detecting volatility regimes.
* A long-term context line to anchor current readings.
* Multi-factor compression analysis to anticipate breakouts.
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Core Features
1. Breach Detection with Amplification
When True Range exceeds a user-defined threshold (e.g., ATR × 1.2), it is amplified using a power function to reflect nonlinear volatility. This amplified value is then smoothed and cascades into future ATR values, affecting the indicator beyond a single bar.
2. Direction Tagging
Volatility spikes are tagged as upward or downward based on basic price momentum (close vs previous close). This provides visual context for how volatility is behaving in real-time.
3. Envelope Zones
Two adaptive envelopes highlight the current volatility regime:
* Stage 1: Moderate volatility (default: ATR × 1.5)
* Stage 2: Extreme volatility (default: ATR × 2.0)
Breaching these zones signals meaningful expansion in volatility.
4. Long-Term Context Baseline
A 200-period simple moving average of the classic ATR establishes whether current readings are above or below long-term volatility expectations.
5. Multi-Signal Compression Detection
Flags potential breakout conditions when:
* ATR is below its long-term baseline
* Price Bollinger Bands are compressed
* RSI Bollinger Bands are also compressed
All three signals must align to plot a "Volatility Confluence Dot" — an early warning of potential expansion.
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Chart Outputs
In the Indicator Pane:
* Breach Amplified ATR (Orange line)
* Classic ATR baseline (White line)
* Long-Term context baseline (Cyan line)
* Stage 1 and Stage 2 Envelopes (Purple and Yellow lines)
On the Price Chart:
* Triangles for breach direction (green/red)
* Diamonds for compression zones
* Optional background coloring for visual clarity
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Alerts
Built-in alert conditions:
1. ATR breach detected
2. Stage 1 envelope breached
3. Stage 2 envelope breached
4. Compression zone detected
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Customization
All components are modular. Traders can adjust:
* Display toggles for each visual layer
* Colors and line widths
* Breach threshold and amplification power
* Envelope sensitivity
* Compression sensitivity and lookback windows
Some options are disabled by default to reduce clutter but can be turned on for more aggressive signal detection.
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Real-Time Behavior (Non-Repainting Clarification)
The indicator updates in real time on the current bar as new data comes in. This is expected behavior for live trading tools. Once a bar closes, values do not change. In other words, the indicator *does not repaint history* — but the current bar can update dynamically until it closes.
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Use Cases
* Day traders: Use compression zones to anticipate volatility surges.
* Swing traders: Use envelope breaches for regime awareness.
* System developers: Replace standard ATR in your logic for better responsiveness.
* Risk managers: Use directional volatility signals to better model exposure.
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About the Developer
Sherlock_MacGyver develops original trading systems that question default assumptions and solve real trader problems.
