Crypto Options Greeks & Volatility Analyzer [BackQuant]Crypto Options Greeks & Volatility Analyzer
Overview
The Crypto Options Greeks & Volatility Analyzer is a comprehensive analytical tool that calculates Black-Scholes option Greeks up to the third order for Bitcoin and Ethereum options. It integrates implied volatility data from VOLMEX indices and provides multiple visualization layers for options risk analysis.
Quick Introduction to Options Trading
Options are financial derivatives that give the holder the right, but not the obligation, to buy or sell an underlying asset at a predetermined price (strike price) within a specific time period (expiration date). Understanding options requires grasping two fundamental concepts:
Call Options : Give the right to buy the underlying asset at the strike price. Calls increase in value when the underlying price rises above the strike price.
Put Options : Give the right to sell the underlying asset at the strike price. Puts increase in value when the underlying price falls below the strike price.
The Language of Options: Greeks
Options traders use "Greeks" - mathematical measures that describe how an option's price changes in response to various factors:
Delta : How much the option price moves for each $1 change in the underlying
Gamma : How fast delta changes as the underlying moves
Theta : Daily time decay - how much value erodes each day
Vega : Sensitivity to implied volatility changes
Rho : Sensitivity to interest rate changes
These Greeks are essential for understanding risk. Just as a pilot needs instruments to fly safely, options traders need Greeks to navigate market conditions and manage positions effectively.
Why Volatility Matters
Implied volatility (IV) represents the market's expectation of future price movement. High IV means:
Options are more expensive (higher premiums)
Market expects larger price swings
Better for option sellers
Low IV means:
Options are cheaper
Market expects smaller moves
Better for option buyers
This indicator helps you visualize and quantify these critical concepts in real-time.
Back to the Indicator
Key Features & Components
1. Complete Greeks Calculations
The indicator computes all standard Greeks using the Black-Scholes-Merton model adapted for cryptocurrency markets:
First Order Greeks:
Delta (Δ) : Measures the rate of change of option price with respect to underlying price movement. Ranges from 0 to 1 for calls and -1 to 0 for puts.
Vega (ν) : Sensitivity to implied volatility changes, expressed as price change per 1% change in IV.
Theta (Θ) : Time decay measured in dollars per day, showing how much value erodes with each passing day.
Rho (ρ) : Interest rate sensitivity, measuring price change per 1% change in risk-free rate.
Second Order Greeks:
Gamma (Γ) : Rate of change of delta with respect to underlying price, indicating how quickly delta will change.
Vanna : Cross-derivative measuring delta's sensitivity to volatility changes and vega's sensitivity to price changes.
Charm : Delta decay over time, showing how delta changes as expiration approaches.
Vomma (Volga) : Vega's sensitivity to volatility changes, important for volatility trading strategies.
Third Order Greeks:
Speed : Rate of change of gamma with respect to underlying price (∂Γ/∂S).
Zomma : Gamma's sensitivity to volatility changes (∂Γ/∂σ).
Color : Gamma decay over time (∂Γ/∂T).
Ultima : Third-order volatility sensitivity (∂²ν/∂σ²).
2. Implied Volatility Analysis
The indicator includes a sophisticated IV ranking system that analyzes current implied volatility relative to its recent history:
IV Rank : Percentile ranking of current IV within its 30-day range (0-100%)
IV Percentile : Percentage of days in the lookback period where IV was lower than current
IV Regime Classification : Very Low, Low, High, or Very High
Color-Coded Headers : Visual indication of volatility regime in the Greeks table
Trading regime suggestions based on IV rank:
IV Rank > 75%: "Favor selling options" (high premium environment)
IV Rank 50-75%: "Neutral / Sell spreads"
IV Rank 25-50%: "Neutral / Buy spreads"
IV Rank < 25%: "Favor buying options" (low premium environment)
3. Gamma Zones Visualization
Gamma zones display horizontal price levels where gamma exposure is highest:
Purple horizontal lines indicate gamma concentration areas
Opacity scaling : Darker shading represents higher gamma values
Percentage labels : Shows gamma intensity relative to ATM gamma
Customizable zones : 3-10 price levels can be analyzed
These zones are critical for understanding:
Pin risk around expiration
Potential for explosive price movements
Optimal strike selection for gamma trading
Market maker hedging flows
4. Probability Cones (Expected Move)
The probability cones project expected price ranges based on current implied volatility:
1 Standard Deviation (68% probability) : Shown with dashed green/red lines
2 Standard Deviations (95% probability) : Shown with dotted green/red lines
Time-scaled projection : Cones widen as expiration approaches
Lognormal distribution : Accounts for positive skew in asset prices
Applications:
Strike selection for credit spreads
Identifying high-probability profit zones
Setting realistic price targets
Risk management for undefined risk strategies
5. Breakeven Analysis
The indicator plots key price levels for options positions:
White line : Strike price
Green line : Call breakeven (Strike + Premium)
Red line : Put breakeven (Strike - Premium)
These levels update dynamically as option premiums change with market conditions.
6. Payoff Structure Visualization
Optional P&L labels display profit/loss at expiration for various price levels:
Shows P&L at -2 sigma, -1 sigma, ATM, +1 sigma, and +2 sigma price levels
Separate calculations for calls and puts
Helps visualize option payoff diagrams directly on the chart
Updates based on current option premiums
Configuration Options
Calculation Parameters
Asset Selection : BTC or ETH (limited by VOLMEX IV data availability)
Expiry Options : 1D, 7D, 14D, 30D, 60D, 90D, 180D
Strike Mode : ATM (uses current spot) or Custom (manual strike input)
Risk-Free Rate : Adjustable annual rate for discounting calculations
Display Settings
Greeks Display : Toggle first, second, and third-order Greeks independently
Visual Elements : Enable/disable probability cones, gamma zones, P&L labels
Table Customization : Position (6 options) and text size (4 sizes)
Price Levels : Show/hide strike and breakeven lines
Technical Implementation
Data Sources
Spot Prices : INDEX:BTCUSD and INDEX:ETHUSD for underlying prices
Implied Volatility : VOLMEX:BVIV (Bitcoin) and VOLMEX:EVIV (Ethereum) indices
Real-Time Updates : All calculations update with each price tick
Mathematical Framework
The indicator implements the full Black-Scholes-Merton model:
Standard normal distribution approximations using Abramowitz and Stegun method
Proper annualization factors (365-day year)
Continuous compounding for interest rate calculations
Lognormal price distribution assumptions
Alert Conditions
Four categories of automated alerts:
Price-Based : Underlying crossing strike price
Gamma-Based : 50% surge detection for explosive moves
Moneyness : Deep ITM alerts when |delta| > 0.9
Time/Volatility : Near expiration and vega spike warnings
Practical Applications
For Options Traders
Monitor all Greeks in real-time for active positions
Identify optimal entry/exit points using IV rank
Visualize risk through probability cones and gamma zones
Track time decay and plan rolls
For Volatility Traders
Compare IV across different expiries
Identify mean reversion opportunities
Monitor vega exposure across strikes
Track higher-order volatility sensitivities
Conclusion
The Crypto Options Greeks & Volatility Analyzer transforms complex mathematical models into actionable visual insights. By combining institutional-grade Greeks calculations with intuitive overlays like probability cones and gamma zones, it bridges the gap between theoretical options knowledge and practical trading application.
Whether you're:
A directional trader using options for leverage
A volatility trader capturing IV mean reversion
A hedger managing portfolio risk
Or simply learning about options mechanics
This tool provides the quantitative foundation needed for informed decision-making in cryptocurrency options markets.
Remember that options trading involves substantial risk and complexity. The Greeks and visualizations provided by this indicator are tools for analysis - they should be combined with proper risk management, position sizing, and a thorough understanding of options strategies.
As crypto options markets continue to mature and grow, having professional-grade analytics becomes increasingly important. This indicator ensures you're equipped with the same analytical capabilities used by institutional traders, adapted specifically for the unique characteristics of 24/7 cryptocurrency markets.
Wyszukaj w skryptach "change"
Bober XM v2.0# ₿ober XM v2.0 Trading Bot Documentation
**Developer's Note**: While our previous Bot 1.3.1 was removed due to guideline violations, this setback only fueled our determination to create something even better. Rising from this challenge, Bober XM 2.0 emerges not just as an update, but as a complete reimagining with multi-timeframe analysis, enhanced filters, and superior adaptability. This adversity pushed us to innovate further and deliver a strategy that's smarter, more agile, and more powerful than ever before. Challenges create opportunity - welcome to Cryptobeat's finest work yet.
## !!!!You need to tune it for your own pair and timeframe and retune it periodicaly!!!!!
## Overview
The ₿ober XM v2.0 is an advanced dual-channel trading bot with multi-timeframe analysis capabilities. It integrates multiple technical indicators, customizable risk management, and advanced order execution via webhook for automated trading. The bot's distinctive feature is its separate channel systems for long and short positions, allowing for asymmetric trade strategies that adapt to different market conditions across multiple timeframes.
### Key Features
- **Multi-Timeframe Analysis**: Analyze price data across multiple timeframes simultaneously
- **Dual Channel System**: Separate parameter sets for long and short positions
- **Advanced Entry Filters**: RSI, Volatility, Volume, Bollinger Bands, and KEMAD filters
- **Machine Learning Moving Average**: Adaptive prediction-based channels
- **Multiple Entry Strategies**: Breakout, Pullback, and Mean Reversion modes
- **Risk Management**: Customizable stop-loss, take-profit, and trailing stop settings
- **Webhook Integration**: Compatible with external trading bots and platforms
### Strategy Components
| Component | Description |
|---------|-------------|
| **Dual Channel Trading** | Uses either Keltner Channels or Machine Learning Moving Average (MLMA) with separate settings for long and short positions |
| **MLMA Implementation** | Machine learning algorithm that predicts future price movements and creates adaptive bands |
| **Pivot Point SuperTrend** | Trend identification and confirmation system based on pivot points |
| **Three Entry Strategies** | Choose between Breakout, Pullback, or Mean Reversion approaches |
| **Advanced Filter System** | Multiple customizable filters with multi-timeframe support to avoid false signals |
| **Custom Exit Logic** | Exits based on OBV crossover of its moving average combined with pivot trend changes |
### Note for Novice Users
This is a fully featured real trading bot and can be tweaked for any ticker — SOL is just an example. It follows this structure:
1. **Indicator** – gives the initial signal
2. **Entry strategy** – decides when to open a trade
3. **Exit strategy** – defines when to close it
4. **Trend confirmation** – ensures the trade follows the market direction
5. **Filters** – cuts out noise and avoids weak setups
6. **Risk management** – controls losses and protects your capital
To tune it for a different pair, you'll need to start from scratch:
1. Select the timeframe (candle size)
2. Turn off all filters and trend entry/exit confirmations
3. Choose a channel type, channel source and entry strategy
4. Adjust risk parameters
5. Tune long and short settings for the channel
6. Fine-tune the Pivot Point Supertrend and Main Exit condition OBV
This will generate a lot of signals and activity on the chart. Your next task is to find the right combination of filters and settings to reduce noise and tune it for profitability.
### Default Strategy values
Default values are tuned for: Symbol BITGET:SOLUSDT.P 5min candle
Filters are off by default: Try to play with it to understand how it works
## Configuration Guide
### General Settings
| Setting | Description | Default Value |
|---------|-------------|---------------|
| **Long Positions** | Enable or disable long trades | Enabled |
| **Short Positions** | Enable or disable short trades | Enabled |
| **Risk/Reward Area** | Visual display of stop-loss and take-profit zones | Enabled |
| **Long Entry Source** | Price data used for long entry signals | hl2 (High+Low/2) |
| **Short Entry Source** | Price data used for short entry signals | hl2 (High+Low/2) |
The bot allows you to trade long positions, short positions, or both simultaneously. Each direction has its own set of parameters, allowing for fine-tuned strategies that recognize the asymmetric nature of market movements.
### Multi-Timeframe Settings
1. **Enable Multi-Timeframe Analysis**: Toggle 'Enable Multi-Timeframe Analysis' in the Multi-Timeframe Settings section
2. **Configure Timeframes**: Set appropriate higher timeframes based on your trading style:
- Timeframe 1: Default is now 15 minutes (intraday confirmation)
- Timeframe 2: Default is 4 hours (trend direction)
3. **Select Sources per Indicator**: For each indicator (RSI, KEMAD, Volume, etc.), choose:
- The desired timeframe (current, mtf1, or mtf2)
- The appropriate price type (open, high, low, close, hl2, hlc3, ohlc4)
### Entry Strategies
- **Breakout**: Enter when price breaks above/below the channel
- **Pullback**: Enter when price pulls back to the channel
- **Mean Reversion**: Enter when price is extended from the channel
You can enable different strategies for long and short positions.
### Core Components
### Risk Management
- **Position Size**: Control risk with percentage-based position sizing
- **Stop Loss Options**:
- Fixed: Set a specific price or percentage from entry
- ATR-based: Dynamic stop-loss based on market volatility
- Swing: Uses recent swing high/low points
- **Take Profit**: Multiple targets with percentage allocation
- **Trailing Stop**: Dynamic stop that follows price movement
## Advanced Usage Strategies
### Moving Average Type Selection Guide
- **SMA**: More stable in choppy markets, good for higher timeframes
- **EMA/WMA**: More responsive to recent price changes, better for entry signals
- **VWMA**: Adds volume weighting for stronger trends, use with Volume filter
- **HMA**: Balance between responsiveness and noise reduction, good for volatile markets
### Multi-Timeframe Strategy Approaches
- **Trend Confirmation**: Use higher timeframe RSI (mtf2) for overall trend, current timeframe for entries
- **Entry Precision**: Use KEMAD on current timeframe with volume filter on mtf1
- **False Signal Reduction**: Apply RSI filter on mtf1 with strict KEMAD settings
### Market Condition Optimization
| Market Condition | Recommended Settings |
|------------------|----------------------|
| **Trending** | Use Breakout strategy with KEMAD filter on higher timeframe |
| **Ranging** | Use Mean Reversion with strict RSI filter (mtf1) |
| **Volatile** | Increase ATR multipliers, use HMA for moving averages |
| **Low Volatility** | Decrease noise parameters, use pullback strategy |
## Webhook Integration
The strategy features a professional webhook system that allows direct connectivity to your exchange or trading platform of choice through third-party services like 3commas, Alertatron, or Autoview.
The webhook payload includes all necessary parameters for automated execution:
- Entry price and direction
- Stop loss and take profit levels
- Position size
- Custom identifier for webhook routing
## Performance Optimization Tips
1. **Start with Defaults**: Begin with the default settings for your timeframe before customizing
2. **Adjust One Component at a Time**: Make incremental changes and test the impact
3. **Match MA Types to Market Conditions**: Use appropriate moving average types based on the Market Condition Optimization table
4. **Timeframe Synergy**: Create logical relationships between timeframes (e.g., 5min chart with 15min and 4h higher timeframes)
5. **Periodic Retuning**: Markets evolve - regularly review and adjust parameters
## Common Setups
### Crypto Trend-Following
- MLMA with EMA or HMA
- Higher RSI thresholds (75/25)
- KEMAD filter on mtf1
- Breakout entry strategy
### Stock Swing Trading
- MLMA with SMA for stability
- Volume filter with higher threshold
- KEMAD with increased filter order
- Pullback entry strategy
### Forex Scalping
- MLMA with WMA and lower noise parameter
- RSI filter on current timeframe
- Use highest timeframe for trend direction only
- Mean Reversion strategy
## Webhook Configuration
- **Benefits**:
- Automated trade execution without manual intervention
- Immediate response to market conditions
- Consistent execution of your strategy
- **Implementation Notes**:
- Requires proper webhook configuration on your exchange or platform
- Test thoroughly with small position sizes before full deployment
- Consider latency between signal generation and execution
### Backtesting Period
Define a specific historical period to evaluate the bot's performance:
| Setting | Description | Default Value |
|---------|-------------|---------------|
| **Start Date** | Beginning of backtest period | January 1, 2025 |
| **End Date** | End of backtest period | December 31, 2026 |
- **Best Practice**: Test across different market conditions (bull markets, bear markets, sideways markets)
- **Limitation**: Past performance doesn't guarantee future results
## Entry and Exit Strategies
### Dual-Channel System
A key innovation of the Bober XM is its dual-channel approach:
- **Independent Parameters**: Each trade direction has its own channel settings
- **Asymmetric Trading**: Recognizes that markets often behave differently in uptrends versus downtrends
- **Optimized Performance**: Fine-tune settings for both bullish and bearish conditions
This approach allows the bot to adapt to the natural asymmetry of markets, where uptrends often develop gradually while downtrends can be sharp and sudden.
### Channel Types
#### 1. Keltner Channels
Traditional volatility-based channels using EMA and ATR:
| Setting | Long Default | Short Default |
|---------|--------------|---------------|
| **EMA Length** | 37 | 20 |
| **ATR Length** | 13 | 17 |
| **Multiplier** | 1.4 | 1.9 |
| **Source** | low | high |
- **Strengths**:
- Reliable in trending markets
- Less prone to whipsaws than Bollinger Bands
- Clear visual representation of volatility
- **Weaknesses**:
- Can lag during rapid market changes
- Less effective in choppy, non-trending markets
#### 2. Machine Learning Moving Average (MLMA)
Advanced predictive model using kernel regression (RBF kernel):
| Setting | Description | Options |
|---------|-------------|--------|
| **Source MA** | Price data used for MA calculations | Any price source (low/high/close/etc.) |
| **Moving Average Type** | Type of MA algorithm for calculations | SMA, EMA, WMA, VWMA, RMA, HMA |
| **Trend Source** | Price data used for trend determination | Any price source (close default) |
| **Window Size** | Historical window for MLMA calculations | 5+ (default: 16) |
| **Forecast Length** | Number of bars to forecast ahead | 1+ (default: 3) |
| **Noise Parameter** | Controls smoothness of prediction | 0.01+ (default: ~0.43) |
| **Band Multiplier** | Multiplier for channel width | 0.1+ (default: 0.5-0.6) |
- **Strengths**:
- Predictive rather than reactive
- Adapts quickly to changing market conditions
- Better at identifying trend reversals early
- **Weaknesses**:
- More computationally intensive
- Requires careful parameter tuning
- Can be sensitive to input data quality
### Entry Strategies
| Strategy | Description | Ideal Market Conditions |
|----------|-------------|-------------------------|
| **Breakout** | Enters when price breaks through channel bands, indicating strong momentum | High volatility, emerging trends |
| **Pullback** | Enters when price retraces to the middle band after testing extremes | Established trends with regular pullbacks |
| **Mean Reversion** | Enters at channel extremes, betting on a return to the mean | Range-bound or oscillating markets |
#### Breakout Strategy (Default)
- **Implementation**: Enters long when price crosses above the upper band, short when price crosses below the lower band
- **Strengths**: Captures strong momentum moves, performs well in trending markets
- **Weaknesses**: Can lead to late entries, higher risk of false breakouts
- **Optimization Tips**:
- Increase channel multiplier for fewer but more reliable signals
- Combine with volume confirmation for better accuracy
#### Pullback Strategy
- **Implementation**: Enters long when price pulls back to middle band during uptrend, short during downtrend pullbacks
- **Strengths**: Better entry prices, lower risk, higher probability setups
- **Weaknesses**: Misses some strong moves, requires clear trend identification
- **Optimization Tips**:
- Use with trend filters to confirm overall direction
- Adjust middle band calculation for market volatility
#### Mean Reversion Strategy
- **Implementation**: Enters long at lower band, short at upper band, expecting price to revert to the mean
- **Strengths**: Excellent entry prices, works well in ranging markets
- **Weaknesses**: Dangerous in strong trends, can lead to fighting the trend
- **Optimization Tips**:
- Implement strong trend filters to avoid counter-trend trades
- Use smaller position sizes due to higher risk nature
### Confirmation Indicators
#### Pivot Point SuperTrend
Combines pivot points with ATR-based SuperTrend for trend confirmation:
| Setting | Default Value |
|---------|---------------|
| **Pivot Period** | 25 |
| **ATR Factor** | 2.2 |
| **ATR Period** | 41 |
- **Function**: Identifies significant market turning points and confirms trend direction
- **Implementation**: Requires price to respect the SuperTrend line for trade confirmation
#### Weighted Moving Average (WMA)
Provides additional confirmation layer for entries:
| Setting | Default Value |
|---------|---------------|
| **Period** | 15 |
| **Source** | ohlc4 (average of Open, High, Low, Close) |
- **Function**: Confirms trend direction and filters out low-quality signals
- **Implementation**: Price must be above WMA for longs, below for shorts
### Exit Strategies
#### On-Balance Volume (OBV) Based Exits
Uses volume flow to identify potential reversals:
| Setting | Default Value |
|---------|---------------|
| **Source** | ohlc4 |
| **MA Type** | HMA (Options: SMA, EMA, WMA, RMA, VWMA, HMA) |
| **Period** | 22 |
- **Function**: Identifies divergences between price and volume to exit before reversals
- **Implementation**: Exits when OBV crosses its moving average in the opposite direction
- **Customizable MA Type**: Different MA types provide varying sensitivity to OBV changes:
- **SMA**: Traditional simple average, equal weight to all periods
- **EMA**: More weight to recent data, responds faster to price changes
- **WMA**: Weighted by recency, smoother than EMA
- **RMA**: Similar to EMA but smoother, reduces noise
- **VWMA**: Factors in volume, helpful for OBV confirmation
- **HMA**: Reduces lag while maintaining smoothness (default)
#### ADX Exit Confirmation
Uses Average Directional Index to confirm trend exhaustion:
| Setting | Default Value |
|---------|---------------|
| **ADX Threshold** | 35 |
| **ADX Smoothing** | 60 |
| **DI Length** | 60 |
- **Function**: Confirms trend weakness before exiting positions
- **Implementation**: Requires ADX to drop below threshold or DI lines to cross
## Filter System
### RSI Filter
- **Function**: Controls entries based on momentum conditions
- **Parameters**:
- Period: 15 (default)
- Overbought level: 71
- Oversold level: 23
- Multi-timeframe support: Current, MTF1 (15min), or MTF2 (4h)
- Customizable price source (open, high, low, close, hl2, hlc3, ohlc4)
- **Implementation**: Blocks long entries when RSI > overbought, short entries when RSI < oversold
### Volatility Filter
- **Function**: Prevents trading during excessive market volatility
- **Parameters**:
- Measure: ATR (Average True Range)
- Period: Customizable (default varies by timeframe)
- Threshold: Adjustable multiplier
- Multi-timeframe support
- Customizable price source
- **Implementation**: Blocks trades when current volatility exceeds threshold × average volatility
### Volume Filter
- **Function**: Ensures adequate market liquidity for trades
- **Parameters**:
- Threshold: 0.4× average (default)
- Measurement period: 5 (default)
- Moving average type: Customizable (HMA default)
- Multi-timeframe support
- Customizable price source
- **Implementation**: Requires current volume to exceed threshold × average volume
### Bollinger Bands Filter
- **Function**: Controls entries based on price relative to statistical boundaries
- **Parameters**:
- Period: Customizable
- Standard deviation multiplier: Adjustable
- Moving average type: Customizable
- Multi-timeframe support
- Customizable price source
- **Implementation**: Can require price to be within bands or breaking out of bands depending on strategy
### KEMAD Filter (Kalman EMA Distance)
- **Function**: Advanced trend confirmation using Kalman filter algorithm
- **Parameters**:
- Process Noise: 0.35 (controls smoothness)
- Measurement Noise: 24 (controls reactivity)
- Filter Order: 6 (higher = more smoothing)
- ATR Length: 8 (for bandwidth calculation)
- Upper Multiplier: 2.0 (for long signals)
- Lower Multiplier: 2.7 (for short signals)
- Multi-timeframe support
- Customizable visual indicators
- **Implementation**: Generates signals based on price position relative to Kalman-filtered EMA bands
## Risk Management System
### Position Sizing
Automatically calculates position size based on account equity and risk parameters:
| Setting | Default Value |
|---------|---------------|
| **Risk % of Equity** | 50% |
- **Implementation**:
- Position size = (Account equity × Risk %) ÷ (Entry price × Stop loss distance)
- Adjusts automatically based on volatility and stop placement
- **Best Practices**:
- Start with lower risk percentages (1-2%) until strategy is proven
- Consider reducing risk during high volatility periods
### Stop-Loss Methods
Multiple stop-loss calculation methods with separate configurations for long and short positions:
| Method | Description | Configuration |
|--------|-------------|---------------|
| **ATR-Based** | Dynamic stops based on volatility | ATR Period: 14, Multiplier: 2.0 |
| **Percentage** | Fixed percentage from entry | Long: 1.5%, Short: 1.5% |
| **PIP-Based** | Fixed currency unit distance | 10.0 pips |
- **Implementation Notes**:
- ATR-based stops adapt to changing market volatility
- Percentage stops maintain consistent risk exposure
- PIP-based stops provide precise control in stable markets
### Trailing Stops
Locks in profits by adjusting stop-loss levels as price moves favorably:
| Setting | Default Value |
|---------|---------------|
| **Stop-Loss %** | 1.5% |
| **Activation Threshold** | 2.1% |
| **Trailing Distance** | 1.4% |
- **Implementation**:
- Initial stop remains fixed until profit reaches activation threshold
- Once activated, stop follows price at specified distance
- Locks in profit while allowing room for normal price fluctuations
### Risk-Reward Parameters
Defines the relationship between risk and potential reward:
| Setting | Default Value |
|---------|---------------|
| **Risk-Reward Ratio** | 1.4 |
| **Take Profit %** | 2.4% |
| **Stop-Loss %** | 1.5% |
- **Implementation**:
- Take profit distance = Stop loss distance × Risk-reward ratio
- Higher ratios require fewer winning trades for profitability
- Lower ratios increase win rate but reduce average profit
### Filter Combinations
The strategy allows for simultaneous application of multiple filters:
- **Recommended Combinations**:
- Trending markets: RSI + KEMAD filters
- Ranging markets: Bollinger Bands + Volatility filters
- All markets: Volume filter as minimum requirement
- **Performance Impact**:
- Each additional filter reduces the number of trades
- Quality of remaining trades typically improves
- Optimal combination depends on market conditions and timeframe
### Multi-Timeframe Filter Applications
| Filter Type | Current Timeframe | MTF1 (15min) | MTF2 (4h) |
|-------------|-------------------|-------------|------------|
| RSI | Quick entries/exits | Intraday trend | Overall trend |
| Volume | Immediate liquidity | Sustained support | Market participation |
| Volatility | Entry timing | Short-term risk | Regime changes |
| KEMAD | Precise signals | Trend confirmation | Major reversals |
## Visual Indicators and Chart Analysis
The bot provides comprehensive visual feedback on the chart:
- **Channel Bands**: Keltner or MLMA bands showing potential support/resistance
- **Pivot SuperTrend**: Colored line showing trend direction and potential reversal points
- **Entry/Exit Markers**: Annotations showing actual trade entries and exits
- **Risk/Reward Zones**: Visual representation of stop-loss and take-profit levels
These visual elements allow for:
- Real-time strategy assessment
- Post-trade analysis and optimization
- Educational understanding of the strategy logic
## Implementation Guide
### TradingView Setup
1. Load the script in TradingView Pine Editor
2. Apply to your preferred chart and timeframe
3. Adjust parameters based on your trading preferences
4. Enable alerts for webhook integration
### Webhook Integration
1. Configure webhook URL in TradingView alerts
2. Set up receiving endpoint on your trading platform
3. Define message format matching the bot's output
4. Test with small position sizes before full deployment
### Optimization Process
1. Backtest across different market conditions
2. Identify parameter sensitivity through multiple tests
3. Focus on risk management parameters first
4. Fine-tune entry/exit conditions based on performance metrics
5. Validate with out-of-sample testing
## Performance Considerations
### Strengths
- Adaptability to different market conditions through dual channels
- Multiple layers of confirmation reducing false signals
- Comprehensive risk management protecting capital
- Machine learning integration for predictive edge
### Limitations
- Complex parameter set requiring careful optimization
- Potential over-optimization risk with so many variables
- Computational intensity of MLMA calculations
- Dependency on proper webhook configuration for execution
### Best Practices
- Start with conservative risk settings (1-2% of equity)
- Test thoroughly in demo environment before live trading
- Monitor performance regularly and adjust parameters
- Consider market regime changes when evaluating results
## Conclusion
The ₿ober XM v2.0 represents a significant evolution in trading strategy design, combining traditional technical analysis with machine learning elements and multi-timeframe analysis. The core strength of this system lies in its adaptability and recognition of market asymmetry.
### Market Asymmetry and Adaptive Approach
The strategy acknowledges a fundamental truth about markets: bullish and bearish phases behave differently and should be treated as distinct environments. The dual-channel system with separate parameters for long and short positions directly addresses this asymmetry, allowing for optimized performance regardless of market direction.
### Targeted Backtesting Philosophy
It's counterproductive to run backtests over excessively long periods. Markets evolve continuously, and strategies that worked in previous market regimes may be ineffective in current conditions. Instead:
- Test specific market phases separately (bull markets, bear markets, range-bound periods)
- Regularly re-optimize parameters as market conditions change
- Focus on recent performance with higher weight than historical results
- Test across multiple timeframes to ensure robustness
### Multi-Timeframe Analysis as a Game-Changer
The integration of multi-timeframe analysis fundamentally transforms the strategy's effectiveness:
- **Increased Safety**: Higher timeframe confirmations reduce false signals and improve trade quality
- **Context Awareness**: Decisions made with awareness of larger trends reduce adverse entries
- **Adaptable Precision**: Apply strict filters on lower timeframes while maintaining awareness of broader conditions
- **Reduced Noise**: Higher timeframe data naturally filters market noise that can trigger poor entries
The ₿ober XM v2.0 provides traders with a framework that acknowledges market complexity while offering practical tools to navigate it. With proper setup, realistic expectations, and attention to changing market conditions, it delivers a sophisticated approach to systematic trading that can be continuously refined and optimized.
Uptrick: MACD Slope Buy/Sell SignalsThe "Uptrick: MACD Slope Buy/Sell Signals" indicator is an advanced technical analysis tool meticulously crafted to provide traders with precise buy and sell signals derived from the slope changes of the Moving Average Convergence Divergence (MACD) signal line. This indicator integrates user-defined parameters for the MACD calculation, including the fast length, slow length, and signal smoothing period. These parameters allow traders to customize the indicator according to their specific trading strategies and timeframes, ensuring adaptability across various market conditions.
The primary function of this indicator is to monitor the slope of the MACD signal line and detect significant shifts that indicate potential changes in market momentum. The indicator calculates the slope by comparing the current value of the signal line to its previous value, and further determines the change in slope to identify acceleration or deceleration in the trend. A buy signal is generated when the slope of the signal line transitions from negative to positive, signaling an upward momentum, while a sell signal is triggered when the slope moves from positive to negative, indicating a downward trend. To enhance signal accuracy, the indicator distinguishes between regular and strong signals. A strong buy signal requires the slope change to be greater than the simple moving average (SMA) of recent slope changes, whereas a strong sell signal necessitates the slope change to be less than the negative SMA of recent slope changes.
A unique feature of this indicator is its dynamic and intuitive visualization. When a strong buy or sell signal is identified, it plots labels ('B' for buy and 'S' for sell) directly on the price chart. These labels are strategically positioned below or above the respective bars to ensure clear visibility and reduce chart clutter. The indicator also includes an option to connect consecutive signals with lines, which enhances the visual tracking of signal sequences and provides a coherent view of the trend's progression. The color intensity of the plotted signals varies based on the absolute value of the slope, offering an immediate visual cue on the strength of the detected trend changes. A steeper slope results in a darker color, signaling a stronger trend.
To facilitate comprehensive analysis, the indicator also plots the MACD and signal lines on the chart, providing traders with a reference to the underlying data that drives the buy and sell signals. These lines are color-coded for easy differentiation: the MACD line is typically blue, and the signal line is orange. This visual aid ensures that traders have a clear understanding of the indicator's basis and can cross-reference the generated signals with the MACD behavior.
The calculation of this indicator is grounded in well-established technical analysis principles. It employs the MACD function to derive the MACD line and signal line based on the user-defined parameters. The slope of the signal line is then computed, followed by the calculation of the slope change. The buy and sell signals are determined by comparing the current and previous slopes, and the strong signals are filtered through an additional layer of slope change analysis relative to its moving average.
The accuracy and reliability of the "Uptrick: MACD Slope Buy/Sell Signals" indicator stem from its thorough and methodical approach to signal generation. By combining user customization, detailed slope analysis, and robust visual elements, this indicator serves as a powerful tool for traders seeking precise entry and exit points in the market. Its ability to adapt to different trading styles and market conditions, coupled with its clear visual cues, makes it a valuable addition to any trader's toolkit, enhancing decision-making and improving trading outcomes.
Machine Learning: VWAP [YinYangAlgorithms]Machine Learning: VWAP aims to use Machine Learning to Identify the best location to Anchor the VWAP at. Rather than using a traditional fixed length or simply adjusting based on a Date / Time; by applying Machine Learning we may hope to identify crucial areas which make sense to reset the VWAP and start anew. VWAP’s may act similar to a Bollinger Band in the sense that they help to identify both Overbought and Oversold Price locations based on previous movements and help to identify how far the price may move within the current Trend. However, unlike Bollinger Bands, VWAPs have the ability to parabolically get quite spaced out and also reset. For this reason, the price may never actually go from the Lower to the Upper and vice versa (when very spaced out; when the Upper and Lower zones are narrow, it may bounce between the two). The reason for this is due to how the anchor location is calculated and in this specific Indicator, how it changes anchors based on price movement calculated within Machine Learning.
This Indicator changes the anchor if the Low < Lowest Low of a length of X and likewise if the High > Highest High of a length of X. This logic is applied within a Machine Learning standpoint that likewise amplifies this Lookback Length by adding a Machine Learning Length to it and increasing the lookback length even further.
Due to how the anchor for this VWAP changes, you may notice that the Basis Line (Orange) may act as a Trend Identifier. When the Price is above the basis line, it may represent a bullish trend; and likewise it may represent a bearish trend when below it. You may also notice what may happen is when the trend occurs, it may push all the way to the Upper or Lower levels of this VWAP. It may then proceed to move horizontally until the VWAP expands more and it may gain more movement; or it may correct back to the Basis Line. If it corrects back to the basis line, what may happen is it either uses the Basis Line as a Support and continues in its current direction, or it will change the VWAP anchor and start anew.
Tutorial:
If we zoom in on the most recent VWAP we can see how it expands. Expansion may be caused by time but generally it may be caused by price movement and volume. Exponential Price movement causes the VWAP to expand, even if there are corrections to it. However, please note Volume adds a large weighted factor to the calculation; hence Volume Weighted Average Price (VWAP).
If you refer to the white circle in the example above; you’ll be able to see that the VWAP expanded even while the price was correcting to the Basis line. This happens due to exponential movement which holds high volume. If you look at the volume below the white circle, you’ll notice it was very large; however even though there was exponential price movement after the white circle, since the volume was low, the VWAP didn’t expand much more than it already had.
There may be times where both Volume and Price movement isn’t significant enough to cause much of an expansion. During this time it may be considered to be in a state of consolidation. While looking at this example, you may also notice the color switch from red to green to red. The color of the VWAP is related to the movement of the Basis line (Orange middle line). When the current basis is > the basis of the previous bar the color of the VWAP is green, and when the current basis is < the basis of the previous bar, the color of the VWAP is red. The color may help you gauge the current directional movement the price is facing within the VWAP.
You may have noticed there are signals within this Indicator. These signals are composed of Green and Red Triangles which represent potential Bullish and Bearish momentum changes. The Momentum changes happen when the Signal Type:
The High/Low or Close (You pick in settings)
Crosses one of the locations within the VWAP.
Bullish Momentum change signals occur when :
Signal Type crosses OVER the Basis
Signal Type crosses OVER the lower level
Bearish Momentum change signals occur when:
Signal Type crosses UNDER the Basis
Signal Type Crosses UNDER the upper level
These signals may represent locations where momentum may occur in the direction of these signals. For these reasons there are also alerts available to be set up for them.
If you refer to the two circles within the example above, you may see that when the close goes above the basis line, how it mat represents bullish momentum. Likewise if it corrects back to the basis and the basis acts as a support, it may continue its bullish momentum back to the upper levels again. However, if you refer to the red circle, you’ll see if the basis fails to act as a support, it may then start to correct all the way to the lower levels, or depending on how expanded the VWAP is, it may just reset its anchor due to such drastic movement.
You also have the ability to disable Machine Learning by setting ‘Machine Learning Type’ to ‘None’. If this is done, it will go off whether you have it set to:
Bullish
Bearish
Neutral
For the type of VWAP you want to see. In this example above we have it set to ‘Bullish’. Non Machine Learning VWAP are still calculated using the same logic of if low < lowest low over length of X and if high > highest high over length of X.
Non Machine Learning VWAP’s change much quicker but may also allow the price to correct from one side to the other without changing VWAP Anchor. They may be useful for breaking up a trend into smaller pieces after momentum may have changed.
Above is an example of how the Non Machine Learning VWAP looks like when in Bearish. As you can see based on if it is Bullish or Bearish is how it favors the trend to be and may likewise dictate when it changes the Anchor.
When set to neutral however, the Anchor may change quite quickly. This results in a still useful VWAP to help dictate possible zones that the price may move within, but they’re also much tighter zones that may not expand the same way.
We will conclude this Tutorial here, hopefully this gives you some insight as to why and how Machine Learning VWAPs may be useful; as well as how to use them.
Settings:
VWAP:
VWAP Type: Type of VWAP. You can favor specific direction changes or let it be Neutral where there is even weight to both. Please note, these do not apply to the Machine Learning VWAP.
Source: VWAP Source. By default VWAP usually uses HLC3; however OHLC4 may help by providing more data.
Lookback Length: The Length of this VWAP when it comes to seeing if the current High > Highest of this length; or if the current Low is < Lowest of this length.
Standard VWAP Multiplier: This multiplier is applied only to the Standard VWMA. This is when 'Machine Learning Type' is set to 'None'.
Machine Learning:
Use Rational Quadratics: Rationalizing our source may be beneficial for usage within ML calculations.
Signal Type: Bullish and Bearish Signals are when the price crosses over/under the basis, as well as the Upper and Lower levels. These may act as indicators to where price movement may occur.
Machine Learning Type: Are we using a Simple ML Average, KNN Mean Average, KNN Exponential Average or None?
KNN Distance Type: We need to check if distance is within the KNN Min/Max distance, which distance checks are we using.
Machine Learning Length: How far back is our Machine Learning going to keep data for.
k-Nearest Neighbour (KNN) Length: How many k-Nearest Neighbours will we account for?
Fast ML Data Length: What is our Fast ML Length? This is used with our Slow Length to create our KNN Distance.
Slow ML Data Length: What is our Slow ML Length? This is used with our Fast Length to create our KNN Distance.
If you have any questions, comments, ideas or concerns please don't hesitate to contact us.
HAPPY TRADING!
Price Percentage Shaded CandlesDescription:
The Price Percentage Shaded Candles indicator (P%SC) is a technical analysis tool designed to represent price candles on a chart with shading intensity based on the percentage change between the open and close prices. This overlay indicator enhances visual analysis by providing a visual representation of price movement intensity.
How it Works:
The P%SC indicator calculates the percentage change between the open and close prices of each candle. It then determines the shading intensity of the price candles based on this percentage change. Higher percentage changes result in darker shading, while lower percentage changes result in lighter shading.
Usage:
To effectively utilize the Price Percentage Shaded Candles indicator, follow these steps:
1. Apply the Price Percentage Shaded Candles indicator to your chart by adding it from the available indicators.
2. Configure the indicator's inputs:
- Specify the color for bullish candles using the "Bullish Color" input.
- Specify the color for bearish candles using the "Bearish Color" input.
3. Observe the shaded candles on the chart:
- Bullish candles are colored with the specified bullish color and shaded according to the percentage change.
- Bearish candles are colored with the specified bearish color and shaded according to the percentage change.
4. Interpret the shaded candles:
- Darker shading indicates a higher percentage change and stronger price movement during the corresponding candle.
- Lighter shading indicates a lower percentage change and weaker price movement during the corresponding candle.
5. Combine the analysis of shaded candles with other technical analysis tools, such as trend lines, support and resistance levels, or candlestick patterns, to identify potential trade setups.
6. Implement appropriate risk management strategies, including setting stop-loss orders and position sizing, to manage your trades effectively and protect your capital.
Note: The Price Percentage Shaded Candles indicator provides insights into the shading intensity of price candles based on percentage changes. However, it is recommended to use this indicator in conjunction with other technical analysis tools and perform thorough analysis before making trading decisions.
Flux-Tensor Singularity [FTS]Flux-Tensor Singularity - Multi-Factor Market Pressure Indicator
The Flux-Tensor Singularity (FTS) is an advanced multi-factor oscillator that combines volume analysis, momentum tracking, and volatility-weighted normalization to identify critical market inflection points. Unlike traditional single-factor indicators, FTS synthesizes price velocity, volume mass, and volatility context into a unified framework that adapts to changing market regimes.
This indicator identifies extreme market conditions (termed "singularities") where multiple confirming factors converge, then uses a sophisticated scoring system to determine directional bias. It is designed for traders seeking high-probability setups with built-in confluence requirements.
THEORETICAL FOUNDATION
The indicator is built on the premise that market time is not constant - different market conditions contain varying levels of information density. A 1-minute bar during a major news event contains far more actionable information than a 1-minute bar during overnight low-volume trading. Traditional indicators treat all bars equally; FTS does not.
The theoretical framework draws conceptual parallels to physics (purely as a mental model, not literal physics):
Volume as Mass: Large volume represents significant market participation and "weight" behind price moves. Just as massive objects have stronger gravitational effects, high-volume moves carry more significance.
Price Change as Velocity: The rate of price movement through price space represents momentum and directional force.
Volatility as Time Dilation: When volatility is high relative to its historical norm, the "information density" of each bar increases. The indicator weights these periods more heavily, similar to how time dilates near massive objects in physics.
This is a pedagogical metaphor to create a coherent mental model - the underlying mathematics are standard financial calculations combined in a novel way.
MATHEMATICAL FRAMEWORK
The indicator calculates a composite singularity value through four distinct steps:
Step 1: Raw Singularity Calculation
S_raw = (ΔP × V) × γ²
Where:
ΔP = Price Velocity = close - close
V = Volume Mass = log(volume + 1)
γ² = Time Dilation Factor = (ATR_local / ATR_global)²
Volume Transformation: Volume is log-transformed because raw volume can have extreme outliers (10x-100x normal). The logarithm compresses these spikes while preserving their significance. This is standard practice in volume analysis.
Volatility Weighting: The ratio of short-term ATR (5 periods) to long-term ATR (user-defined lookback) is squared to create a volatility amplification factor. When local volatility exceeds global volatility, this ratio increases, amplifying the raw singularity value. This makes the indicator regime-aware.
Step 2: Normalization
The raw singularity values are normalized to a 0-100 scale using a stochastic-style calculation:
S_normalized = ((S_raw - S_min) / (S_max - S_min)) × 100
Where S_min and S_max are the lowest and highest raw singularity values over the lookback period.
Step 3: Epsilon Compression
S_compressed = 50 + ((S_normalized - 50) / ε)
This is the critical innovation that makes the sensitivity control functional. By applying compression AFTER normalization, the epsilon parameter actually affects the final output:
ε < 1.0: Expands range (more signals)
ε = 1.0: No change (default)
ε > 1.0: Compresses toward 50 (fewer, higher-quality signals)
For example, with ε = 2.0, a normalized value of 90 becomes 70, making threshold breaches rarer and more significant.
Step 4: Smoothing
S_final = EMA(S_compressed, smoothing_period)
An exponential moving average removes high-frequency noise while preserving trend.
SIGNAL GENERATION LOGIC
When the tensor crosses above the upper threshold (default 90) or below the lower threshold (default 10), an extreme event is detected. However, the indicator does NOT immediately generate a buy or sell signal. Instead, it analyzes market context through a multi-factor scoring system:
Scoring Components:
Price Structure (+1 point): Current bar bullish/bearish
Momentum (+1 point): Price higher/lower than N bars ago
Trend Context (+2 points): Fast EMA above/below slow EMA (weighted heavier)
Acceleration (+1 point): Rate of change increasing/decreasing
Volume Multiplier (×1.5): If volume > average, multiply score
The highest score (bullish vs bearish) determines signal direction. This prevents the common indicator failure mode of "overbought can stay overbought" by requiring directional confirmation.
Signal Conditions:
A BUY signal requires:
Extreme event detection (tensor crosses threshold)
Bullish score > Bearish score
Price confirmation: Bullish candle (optional, user-controlled)
Volume confirmation: Volume > average (optional, user-controlled)
Momentum confirmation: Positive momentum (optional, user-controlled)
A SELL signal requires the inverse conditions.
INPUTS EXPLAINED - Core Parameters:
Global Horizon (Context): Default 20. Lookback period for normalization and volatility comparison. Higher values = smoother but less responsive. Lower values = more signals but potentially more noise.
Tensor Smoothing: Default 3. EMA period applied to final output. Removes "quantum foam" (high-frequency noise). Range 1-20.
Singularity Threshold: Default 90. Values above this (or below 100-threshold) trigger extreme event detection. Higher = rarer, stronger signals.
Signal Sensitivity (Epsilon): Default 1.0. Post-normalization compression factor. This is the key innovation - it actually works because it's applied AFTER normalization. Range 0.1-5.0.
Signal Interpreter Toggles:
Require Price Confirmation: Default ON. Only generates buy signals on bullish candles, sell signals on bearish candles. Reduces false signals but may delay entry.
Require Volume Confirmation: Default ON. Only signals when volume > average. Critical for stocks/crypto, less important for forex (unreliable volume data).
Use Momentum Filter: Default ON. Requires momentum agreement with signal direction. Prevents counter-trend signals.
Momentum Lookback: Default 5. Number of bars for momentum calculation. Shorter = more responsive, longer = trend-following bias.
Visual Controls:
Colors: Customizable colors for bullish flux, bearish flux, background, and event horizon.
Visual Transparency: Default 85. Master control for all visual elements (accretion disk, field lines, particles, etc.). Range 50-99. Signals and dashboard have separate controls.
Visibility Toggles: Individual on/off switches for:
Gravitational field lines (trend EMAs)
Field reversals (trend crossovers)
Accretion disk (background gradient)
Singularity diamonds (neutral extreme events)
Energy particles (volume bursts)
Event horizon flash (extreme event background)
Signal background flash
Signal Size: Tiny/Small/Normal triangle size
Signal Offsets: Separate controls for buy and sell signal vertical positioning (percentage of price)
Dashboard Settings:
Show Dashboard: Toggle on/off
Position: 9 placement options (all corners, centers, middles)
Text Size: Tiny/Small/Normal/Large
Background Transparency: 0-50, separate from visual transparency
VISUAL ELEMENTS EXPLAINED
1. Accretion Disk (Background Gradient):
A three-layer gradient background that intensifies as the tensor approaches extremes. The outer disk appears at any non-neutral reading, the inner disk activates above 70 or below 30, and the core layer appears above 85 or below 15. Color indicates direction (cyan = bullish, red = bearish). This provides instant visual feedback on market pressure intensity.
2. Gravitational Field Lines (EMAs):
Two trend-following EMAs (10 and 30 period) visualized as colored lines. These represent the "curvature" of market trend - when they diverge, trend is strong; when they converge, trend is weakening. Crossovers mark potential trend reversals.
3. Field Reversals (Circles):
Small circles appear when the fast EMA crosses the slow EMA, indicating a potential trend change. These are distinct from extreme events and appear at normal market structure shifts.
4. Singularity Diamonds:
Small diamond shapes appear when the tensor reaches extreme levels (>90 or <10) but doesn't meet the full signal criteria. These are "watch" events - extreme pressure exists but directional confirmation is lacking.
5. Energy Particles (Dots):
Tiny dots appear when volume exceeds 2× average, indicating significant participation. Color matches bar direction. These highlight genuine high-conviction moves versus low-volume drifts.
6. Event Horizon Flash:
A golden background flash appears the instant any extreme threshold is breached, before directional analysis. This alerts you to pay attention.
7. Signal Background Flash:
When a full buy/sell signal is confirmed, the background flashes cyan (buy) or red (sell). This is your primary alert that all conditions are met.
8. Signal Triangles:
The actual buy (▲) and sell (▼) markers. These only appear when ALL selected confirmation criteria are satisfied. Position is offset from bars to avoid overlap with other indicators.
DASHBOARD METRICS EXPLAINED
The dashboard displays real-time calculated values:
Event Density: Current tensor value (0-100). Above 90 or below 10 = critical. Icon changes: 🔥 (extreme high), ❄️ (extreme low), ○ (neutral).
Time Dilation (γ): Current volatility ratio squared. Values >2.0 indicate extreme volatility environments. >1.5 = elevated, >1.0 = above average. Icon: ⚡ (extreme), ⚠ (elevated), ○ (normal).
Mass (Vol): Log-transformed volume value. Compared to volume ratio (current/average). Icon: ● (>2× avg), ◐ (>1× avg), ○ (below avg).
Velocity (ΔP): Raw price change. Direction arrow indicates momentum direction. Shows the actual price delta value.
Bullish Flux: Current bullish context score. Displayed as both a bar chart (visual) and numeric value. Brighter when bullish score dominates.
Bearish Flux: Current bearish context score. Same visualization as bullish flux. These scores compete - the winner determines signal direction.
Field: Trend direction based on EMA relationship. "Repulsive" (uptrend), "Attractive" (downtrend), "Neutral" (ranging). Icon: ⬆⬇↔
State: Current market condition:
🚀 EJECTION: Buy signal active
💥 COLLAPSE: Sell signal active
⚠ CRITICAL: Extreme event, no directional confirmation
● STABLE: Normal market conditions
HOW TO USE THE INDICATOR
1. Wait for Extreme Events:
The indicator is designed to be selective. Don't trade every fluctuation - wait for tensor to reach >90 or <10. This alone is not a signal.
2. Check Context Scores:
Look at the Bullish Flux vs Bearish Flux in the dashboard. If scores are close (within 1-2 points), the market is indecisive - skip the trade.
3. Confirm with Signals:
Only act when a full triangle signal appears (▲ or ▼). This means ALL your selected confirmation criteria have been met.
4. Use with Price Structure:
Combine with support/resistance levels. A buy signal AT support is higher probability than a buy signal in the middle of nowhere.
5. Respect the Dashboard State:
When State shows "CRITICAL" (⚠), it means extreme pressure exists but direction is unclear. These are the most dangerous moments - wait for resolution.
6. Volume Matters:
Energy particles (dots) and the Mass metric tell you if institutions are participating. Signals without volume confirmation are lower probability.
MARKET AND TIMEFRAME RECOMMENDATIONS
Scalping (1m-5m):
Lookback: 10-14
Smoothing: 5-7
Threshold: 85
Epsilon: 0.5-0.7
Note: Expect more noise. Confirm with Level 2 data. Best on highly liquid instruments.
Intraday (15m-1h):
Lookback: 20-30 (default settings work well)
Smoothing: 3-5
Threshold: 90
Epsilon: 1.0
Note: Sweet spot for the indicator. High win rate on liquid stocks, forex majors, and crypto.
Swing Trading (4h-1D):
Lookback: 30-50
Smoothing: 3
Threshold: 90-95
Epsilon: 1.5-2.0
Note: Signals are rare but high conviction. Combine with higher timeframe trend analysis.
Position Trading (1D-1W):
Lookback: 50-100
Smoothing: 5-7
Threshold: 95
Epsilon: 2.0-3.0
Note: Extremely rare signals. Only trade the most extreme events. Expect massive moves.
Market-Specific Settings:
Forex (EUR/USD, GBP/USD, etc.):
Volume data is unreliable (spot forex has no centralized volume)
Disable "Require Volume Confirmation"
Focus on momentum and trend filters
News events create extreme singularities
Best on 15m-1h timeframes
Stocks (High-Volume Equities):
Volume confirmation is CRITICAL - keep it ON
Works excellently on AAPL, TSLA, SPY, etc.
Morning session (9:30-11:00 ET) shows highest event density
Earnings announcements create guaranteed extreme events
Best on 5m-1h for day trading, 1D for swing trading
Crypto (BTC, ETH, major alts):
Reduce threshold to 85 (crypto has constant high volatility)
Volume spikes are THE primary signal - keep volume confirmation ON
Works exceptionally well due to 24/7 trading and high volatility
Epsilon can be reduced to 0.7-0.8 for more signals
Best on 15m-4h timeframes
Commodities (Gold, Oil, etc.):
Gold responds to macro events (Fed announcements, geopolitical events)
Oil responds to supply shocks
Use daily timeframe minimum
Increase lookback to 50+
These are slow-moving markets - be patient
Indices (SPX, NDX, etc.):
Institutional volume matters - keep volume confirmation ON
Opening hour (9:30-10:30 ET) = highest singularity probability
Strong correlation with VIX - high VIX = more extreme events
Best on 15m-1h for day trading
WHAT MAKES THIS INDICATOR UNIQUE
1. Post-Normalization Sensitivity Control:
Unlike most oscillators where sensitivity controls don't actually work (they're applied before normalization, which then rescales everything), FTS applies epsilon compression AFTER normalization. This means the sensitivity parameter genuinely affects signal frequency. This is a novel implementation not found in standard oscillators.
2. Multi-Factor Confluence Requirement:
The indicator doesn't just detect "overbought" or "oversold" - it detects extreme conditions AND THEN analyzes context through five separate factors (price structure, momentum, trend, acceleration, volume). Most indicators are single-factor; FTS requires confluence.
3. Volatility-Weighted Normalization:
By squaring the ATR ratio (local/global), the indicator adapts to changing market regimes. A 1% move in a low-volatility environment is treated differently than a 1% move in a high-volatility environment. Traditional indicators treat all moves equally regardless of context.
4. Volume Integration at the Core:
Volume isn't an afterthought or optional filter - it's baked into the fundamental equation as "mass." The log transformation handles outliers elegantly while preserving significance. Most price-based indicators completely ignore volume.
5. Adaptive Scoring System:
Rather than fixed buy/sell rules ("RSI >70 = sell"), FTS uses competitive scoring where bullish and bearish evidence compete. The winner determines direction. This solves the classic problem of "overbought markets can stay overbought during strong uptrends."
6. Comprehensive Visual Feedback:
The multi-layer visualization system (accretion disk, field lines, particles, flashes) provides instant intuitive feedback on market state without requiring dashboard reading. You can see pressure building before extreme thresholds are hit.
7. Separate Extreme Detection and Signal Generation:
"Singularity diamonds" show extreme events that don't meet full criteria, while "signal triangles" only appear when ALL conditions are met. This distinction helps traders understand when pressure exists versus when it's actionable.
COMPARISON TO EXISTING INDICATORS
vs. RSI/Stochastic:
These normalize price relative to recent range. FTS normalizes (price change × log volume × volatility ratio) - a composite metric, not just price position.
vs. Chaikin Money Flow:
CMF combines price and volume but lacks volatility context and doesn't use adaptive normalization or post-normalization compression.
vs. Bollinger Bands + Volume:
Bollinger Bands show volatility but don't integrate volume or create a unified oscillator. They're separate components, not synthesized.
vs. MACD:
MACD is pure momentum. FTS combines momentum with volume weighting and volatility context, plus provides a normalized 0-100 scale.
The specific combination of log-volume weighting, squared volatility amplification, post-normalization epsilon compression, and multi-factor directional scoring is unique to this indicator.
LIMITATIONS AND PROPER DISCLOSURE
Not a Holy Grail:
No indicator is perfect. This tool identifies high-probability setups but cannot predict the future. Losses will occur. Use proper risk management.
Requires Confirmation:
Best used in conjunction with price action analysis, support/resistance levels, and higher timeframe trend. Don't trade signals blindly.
Volume Data Dependency:
On forex (spot) and some low-volume instruments, volume data is unreliable or tick-volume only. Disable volume confirmation in these cases.
Lagging Components:
The EMA smoothing and trend filters are inherently lagging. In extremely fast moves, signals may appear after the initial thrust.
Extreme Event Rarity:
With conservative settings (high threshold, high epsilon), signals can be rare. This is by design - quality over quantity. If you need more frequent signals, reduce threshold to 85 and epsilon to 0.7.
Not Financial Advice:
This indicator is an analytical tool. All trading decisions and their consequences are solely your responsibility. Past performance does not guarantee future results.
BEST PRACTICES
Don't trade every singularity - wait for context confirmation
Higher timeframes = higher reliability
Combine with support/resistance for entry refinement
Volume confirmation is CRITICAL for stocks/crypto (toggle off only for forex)
During major news events, singularities are inevitable but direction may be uncertain - use wider stops
When bullish and bearish flux scores are close, skip the trade
Test settings on your specific instrument/timeframe before live trading
Use the dashboard actively - it contains critical diagnostic information
Taking you to school. — Dskyz, Trade with insight. Trade with anticipation.
Options Greeks AnalyzerOptions Greeks Analyzer (Training & Learning Guide)
________________________________________
1. Introduction
Options trading is advanced compared to regular stock trading, and one of the most important aspects is Options Greeks. Greeks are mathematical values that measure how the price of an option will react to changes in various factors such as the underlying asset’s price, volatility, interest rates, and time to expiry.
This Options Greeks Analyzer tool is built using TradingView Pine Script v5. It serves as a real time training and analysis dashboard that helps learners visualize how options greeks behave, how option prices change, and how traders can make informed decisions.
📌 Educational Disclaimer:
This tool is only for training and learning purposes. It is not a financial advice tool nor to be used for live trading decisions. The data shown is theoretical Black Scholes model calculations, which may differ from actual option market prices.
________________________________________
2. How the Tool Works
The Options Greeks Analyzer is divided into different modules. Below is a step by step walkthrough:
________________________________________
Step 1: User Inputs
• Implied Volatility (IV%) — You can manually enter volatility, which is the most important factor in option pricing. Higher IV = higher option premium.
• Expiry Selection — Choose from preset durations like 7D, 14D, 30D etc. Days to expiry directly affect time decay (Theta).
• Strike Price Mode — You can select either:
o ATM (At-the-Money = Current price of stock/index)
o Custom strike (Enter your own strike price)
• Risk-Free Rate (%) — A small interest rate factor (like government bond yield) used for theoretical valuation.
• Table Customization — Choose table size, position, and whether to show price lines for easy visibility.
________________________________________
Step 2: Market Data & Volatility
• The tool takes the current market price (Spot Price) as input.
• It calculates realized volatility from historical price fluctuations (using past 30 bars/log returns).
• Implied Volatility (manual input) is then compared to realized vol:
o If IV > Historical Volatility → Market pricing is “expensive” (HIGH IV RANK).
o If IV < Historical Volatility → Market is “cheap” (LOW IV RANK).
o Otherwise, it’s MEDIUM.
📌 Why it matters?
Traders can decide whether buying or selling options is favorable. Beginners learn that timing entry with volatility is more critical than just looking at market direction.
________________________________________
Step 3: Black-Scholes Formula
The core engine uses the Black-Scholes model, a mathematical formula widely used to compute option fair prices.
It uses the following inputs:
• Current price (Spot)
• Strike Price
• Time to Expiry (T)
• Risk Free Rate (r)
• Implied Volatility (σ)
This produces:
• Call Option Price
• Put Option Price
📌 This teaches learners how premiums are derived theoretically and why the same strike can have different values depending on IV and time.
________________________________________
Step 4: Option Greeks Calculation
The tool computes the first order Greeks:
• Delta → Measures how much the option price changes when the underlying stock moves by 1 point.
(Call Delta ranges 0–1, Put Delta ranges -1 to 0).
• Gamma → Sensitivity of Delta to price change. A measure of volatility risk.
• Theta → Time decay. Shows how much value option loses as each day passes. Calls and Puts have negative Theta (decay).
• Vega → Measures how sensitive option price is to volatility changes.
• Rho → Interest rate sensitivity. Mostly minor in equity options but important for training.
📌 New traders learn how each factor impacts profits/losses. Instead of random guessing, they see mathematical impact in numbers.
________________________________________
Step 5: Dashboard & Visualization
The tool builds a professional dashboard table on the chart.
It shows categories such as:
1. Asset Info — Spot, Strike, DTE (days to expiry), IV%, IV Rank, 1-Day Trend, Moneyness (ATM/OTM/ITM).
2. Option Prices — Call, Put, Break-even levels, Time Value, Expected Move (%), Realized vs Implied Vol.
3. Greeks with Visual Progress Bars — Easily shows Delta, Gamma, Vega, Theta, Rho in intuitive graphical representations.
4. Status Bar — Suggests theoretical bias like:
o HIGH IV → Favor Option Selling
o LOW IV → Favor Option Buying
o MEDIUM → Neutral observation
5. Recommendation Line — Offers training-based suggestions like “Buy Straddles”, “Sell Call Spreads”, etc. These are not signals, but scenarios to learn strategies.
________________________________________
3. How It Helps Beginners
1. Learn Greeks in Action:
Beginners often memorize formulas but never see real-time changes. This dashboard updates every bar to show how Greeks change dynamically.
2. Compare Volatilities:
Traders understand difference between historical vs implied volatility and why option premiums behave differently.
3. Understand Risk Levels:
The tool highlights when Gamma risk is high (danger for sellers) or when Theta is most favorable.
4. Training Mode for Strategies:
Helps beginners experiment by changing IV, strike, expiry and seeing how straddles, spreads, naked options would behave theoretically.
5. Prepares Before Live Trading:
Safe environment to practice option analysis without risking capital.
________________________________________
4. Educational Use Cases
• Scenario 1: Change expiry from 7D to 30D — see how Theta becomes slower for longer expiries.
• Scenario 2: Increase IV from 25% to 80% — watch how option premiums inflate, and recommendation changes from “Buy” to “Sell”.
• Scenario 3: Select OTM vs ITM strikes — check how delta moves from near 0 to near 1.
By running these scenarios, learners understand why professional traders hedge Greeks instead of directional gambling.
________________________________________
5. Disclaimer
This Options Greeks Analyzer is built strictly for educational and training purposes.
• It uses theoretical formulas (Black-Scholes) that may not match actual option market prices.
• The recommendations are for learning strategy logic only, not real-world execution signals.
• Trading in options carries significant risks and may result in capital loss.
📌 Always consult with a financial advisor before applying real strategies.
________________________________________
✅ Summary
This Options Greeks Analyzer:
• Teaches how Greeks, IV, and premiums work.
• Provides a real-time interactive dashboard for training.
• Helps beginners practice option scenarios safely.
• Is meant strictly for learning and not live trading execution.
________________________________________
________________________________________
Disclaimer from aiTrendview
This script and its trading signals are provided for training and educational purposes only. They do not constitute financial advice or a guaranteed trading system. Trading involves substantial risk, and there is the potential to lose all invested capital. Users should perform their own analysis and consult with qualified financial professionals before making any trading decisions. aiTrendview disclaims any liability for losses incurred from using this code or trading based on its signals. Use this tool responsibly, and trade only with risk capital.
Ichimoku Cloud Signals [sgbpulse] Ichimoku Cloud Signals – Your Advanced Trading Tool
Meet Ichimoku Cloud Signals, the enhanced and interactive version of the classic Ichimoku Cloud indicator, designed specifically for TradingView traders seeking precision and flexibility in their trading decisions. This indicator allows you to maximize the Ichimoku's potential by customizing trend criteria, receiving clear visual signals for entering and exiting positions, and getting alerts to keep you informed.
Introduction to the Ichimoku Cloud
The Ichimoku Cloud, also known as Ichimoku Kinko Hyo, is a comprehensive technical analysis tool developed in Japan. It provides a broad view of the market: trend direction, momentum, and support and resistance levels. "Ichimoku Cloud Signals" takes this power and amplifies it with advanced features.
Key Components of the Ichimoku Cloud
The indicator displays all five familiar Ichimoku lines, along with the "Cloud" (Kumo):
Tenkan-sen (Conversion Line): Calculated as the average of the highest high and lowest low over the past 9 periods. A fast, short-term indicator used as a measure of immediate momentum.
Kijun-sen (Base Line): Calculated as the average of the highest high and lowest low over the past 26 periods. A medium-term reference line serving as a significant support/resistance level.
Senkou Span A (Leading Span A): The average of the Tenkan-sen and Kijun-sen, shifted 26 periods forward into the future.
Senkou Span B (Leading Span B): The average of the highest high and lowest low over the past 52 periods, also shifted 26 periods forward into the future.
Kumo (Cloud): The area between Senkou Span A and Senkou Span B. Its color changes: green for an uptrend (when Senkou Span A is above Senkou Span B) and red for a downtrend (when Senkou Span B is above Senkou Span A). The Cloud serves as a dynamic area of support/resistance and a tool for forecasting future trends.
Chikou Span (Lagging Span): The current closing price, shifted 26 periods backward into the past. It serves as a powerful trend confirmation tool.
How the Ichimoku Cloud Works and How to Interpret It
Trend Identification :
- Uptrend (Bullish): The price is above the Cloud. The higher the price is above the Cloud, the stronger the trend.
- Downtrend (Bearish): The price is below the Cloud. The lower the price is below the Cloud, the stronger the trend.
- Range/Consolidation: The price is within the Cloud. This indicates a market without a clear direction or one that is consolidating.
Support and Resistance:
- The Cloud itself acts as a dynamic area of support and resistance. In an uptrend, the Cloud serves as support. In a downtrend, it serves as resistance.
- A thick Cloud indicates stronger support/resistance levels, while a thin Cloud indicates weaker levels.
The Cloud as a Predictive Indicator:
The uniqueness of the Kumo (Cloud) lies in its ability to be shifted 26 periods forward. This part of the Cloud provides forecasts for future support and resistance levels and even suggests expected trend changes (like a "Kumo Twist" – a change in Cloud color), giving you a planning advantage.
Unique Advantages of Ichimoku Cloud Signals:
Ichimoku Cloud Signals takes the classic Ichimoku principles and gives you unprecedented control:
Focused Trend Selection:
Choose whether you want to analyze a bullish (uptrend) or bearish (downtrend) trend. The indicator will focus on the relevant criteria for your selection.
Customizable Trend Confirmation Criteria (8 Criteria):
The indicator relies on 8 key criteria for clear trend confirmation. You can enable or disable each criterion individually based on your trading strategy and desired risk level. Each criterion plays a vital role in confirming the strength of the trend:
- Price position relative to the Cloud (Kumo) (Default: true): Determines the main trend direction and whether it's bullish or bearish.
- Price position relative to Kijun-sen (Base Line) (Default: true): Indicates the medium-term trend and acts as a critical equilibrium level.
- Price position relative to Tenkan-sen (Conversion Line) (Default: false): Provides quick confirmation of current momentum and short-term market changes.
- Tenkan-sen (Conversion Line) / Kijun-sen (Base Line) Crossover (Default: true): A classic signal for momentum change, crucial for identifying entry points.
- Current Cloud trend (Kumo) (Default: false): Cloud color confirms the main trend direction in real-time.
- Projected Future Cloud trend (Kumo) (Default: true): Indicates an expected future change in the Cloud's trend, providing strong visual insight.
- Chikou Span (Lagging Span) position relative to the Cloud (Kumo) (Default: true): Confirms the current trend strength by comparing the price to the Ichimoku 26 periods ago.
- Chikou Span (Lagging Span) position relative to the Price (Default: false): Additional confirmation of trend strength, indicating buyer/seller dominance.
Full Customization of Ichimoku Parameters:
You can change the period lengths for each Ichimoku component, depending on your strategy:
- Conversion Line Length (Default: 9)
- Base Line Length (Default: 26)
- Leading Span Length (Default: 52)
- Cloud Lagging Length (Default: 26)
- Lagging Span Length (Default: 26)
Visual Criteria Table on the Chart:
Get immediate and clear feedback! A visual table is placed on the chart, showing in real-time which of the 8 criteria you have defined are met for your chosen trend. Criteria you have enabled will be highlighted with a blue color and a "➤" symbol, while disabled criteria will appear in a subtle gray shade. For each criterion, the table shows its real-time status with a "✔" symbol if the condition is met and an "✘" symbol if it is not met. This powerful visual tool provides a quick assessment, helps with learning, and allows for strategy optimization at the click of a button.
Precise Criteria Details in the Data Window:
Beyond the visual table, the indicator provides an additional critical layer of detail: for any point on the chart, you can hover over a candle and see in TradingView's Data Window the precise status and values of all eight criteria. For each criterion, you'll see a clear numerical value (1 or 0) indicating whether it's fully met (1) or not met (0). Additionally, you can inspect the exact numerical values of the Ichimoku lines (Tenkan-sen, Kijun-sen, etc.) at that specific moment. This comprehensive data supports in-depth analysis, strategy debugging, and long-term optimization, providing complete transparency regarding every component of the signal.
Smart and Customizable Alerts:
Ichimoku Cloud Signals provides a powerful alert system to keep you informed of key market movements, so you never miss an opportunity. There are eight unique alerts you can enable in TradingView's alert panel:
Uptrend Entry Alert: Triggers when all of your selected criteria for an uptrend are met on a new candle.
Uptrend Exit Alert: Triggers when one of your selected uptrend criteria is no longer met, signaling a potential exit point.
Downtrend Entry Alert: Triggers when all of your selected criteria for a downtrend are met on a new candle.
Downtrend Exit Alert: Triggers when one of your selected downtrend criteria is no longer met, signaling a potential exit point.
Bullish Crossover Alert: Triggers when the Conversion Line (Tenkan-sen) crosses above the Base Line (Kijun-sen), a classic signal for an upward momentum shift.
Bearish Crossover Alert: Triggers when the Conversion Line (Tenkan-sen) crosses below the Base Line (Kijun-sen), signaling a potential shift to downward momentum.
Bullish Cloud Breakout Alert: Triggers when the price closes above the Ichimoku Cloud (Kumo), indicating a strong bullish trend.
Bearish Cloud Breakout Alert: Triggers when the price closes below the Ichimoku Cloud (Kumo), indicating a strong bearish trend.
Each alert can be independently configured in TradingView's alert panel, allowing you to tailor your notifications to fit your exact trading strategy and risk management preferences.
Summary:
Ichimoku Cloud Signals is an essential tool for TradingView traders seeking control, clarity, and precision. It combines the power of the classic Ichimoku Cloud indicator with advanced customization capabilities, a convenient visual table, and clear signals, empowering you to make informed trading decisions and stay focused on managing your positions.
Important Note: Trading Risk
This indicator is intended for educational and informational purposes only and does not constitute investment advice or a recommendation for trading in any form whatsoever.
Trading in financial markets involves significant risk of capital loss. It is important to remember that past performance is not indicative of future results. All trading decisions are your sole responsibility. Never trade with money you cannot afford to lose.
BANKNIFTY Contribution Table [GSK-VIZAG-AP-INDIA]1. Overview
This indicator provides a real-time visual contribution table of the 12 constituent stocks in the BANKNIFTY index. It displays key metrics for each stock that help traders quickly understand how each component is impacting the index at any given moment.
2. Purpose / Trading Use Case
The tool is designed for intraday and short-term traders who rely on index movement and its internal strength or weakness. By seeing which stocks are contributing positively or negatively, traders can:
Confirm trend strength or divergence within the index.
Identify whether a BANKNIFTY move is broad-based or driven by a few heavyweights.
Detect reversals when individual components decouple from index direction.
3. Key Features and Logic
Live LTP: Current price of each BANKNIFTY stock.
Price Change: Difference between current LTP and previous day’s close.
% Change: Percentage move from previous close.
Weight %: Static weight of each stock within the BANKNIFTY index (user-defined).
This estimates how much each stock contributes to the BANKNIFTY’s point change.
Sorted View: The stocks are sorted by their weight (descending), so high-impact movers are always at the top.
4. User Inputs / Settings
Table Position (tableLocationOpt):
Choose where the table appears on the chart:
top_left, top_right, bottom_left, or bottom_right.
This helps position the table away from your price action or indicators.
5. Visual and Plotting Elements
Table Layout: 6 columns
Stock | Contribution | Weight % | LTP | Change | % Change
Color Coding:
Green/red for positive/negative price changes and contributions.
Alternating background rows for better visibility.
BANKNIFTY row is highlighted separately at the top.
Text & Background Colors are chosen for both readability and direction indication.
6. Tips for Effective Use
Use this table on 1-minute or 5-minute intraday charts to see near real-time market structure.
Watch for:
A few heavyweight stocks pulling the index alone (can signal weak internal breadth).
Broad green/red across all rows (signals strong directional momentum).
Combine this with price action or volume-based strategies for confirmation.
Best used during market hours for live updates.
7. What Makes It Unique
Unlike other contribution tables that show only static data or require paid feeds, this script:
Updates in real time.
Uses dynamic calculated contributions.
Places BANKNIFTY at the top and presents the entire internal structure clearly.
Doesn’t repaint or rely on lagging indicators.
8. Alerts / Additional Features
No alerts are added in this version.
(Optional: Alerts can be added to notify when a certain stock contributes above/below a threshold.)
9. Technical Concepts Used
request.security() to pull both 1-minute and daily close data.
Conditional color formatting based on price change direction.
Dynamic table rendering using table.new() and table.cell().
Static weights assigned manually for BANKNIFTY stocks (can be updated if index weights change).
10. Disclaimer
This script is intended for educational and informational purposes only. It does not constitute financial advice or a buy/sell recommendation.
Users should test and validate the tool on paper or demo accounts before applying it to live trading.
📌 Note: Due to internet connectivity, data delays, or broker feeds, real-time values (LTP, change, contribution, etc.) may slightly differ from other platforms or terminals. Use this indicator as a supportive visual tool, not a sole decision-maker.
Script Title: BANKNIFTY Contribution Table -
Author: GSK-VIZAG-AP-INDIA
Version: Final Public Release
Color Code Overlay StrategyColor Code Overlay Strategy
This strategy utilizes a custom color-coded overlay to provide accurate buy and sell signals based on dynamic color changes of the candles. The indicator works by calculating a color shift between bullish (green) and bearish (red) candles, with the color change logic driven by both price movement and volatility.
How the Color Change is Calculated:
The color change is determined by comparing the closing price relative to the opening price of each candle, as is typical with a traditional bullish or bearish candle. However, to make this strategy more adaptive to market conditions, the color change is further refined by incorporating the Average True Range (ATR).
Volatility Adjusted Color Shift: The strategy calculates a dynamic threshold based on the ATR value, which represents market volatility. If the price movement between the open and close of the candle exceeds a specific percentage of the ATR, the color of the candle shifts from red (bearish) to green (bullish) or vice versa.
Threshold Calculation: A fixed percentage (e.g., 1%) of the ATR range is used to define the minimum price movement required for a color change. This ensures that only significant price movements, adjusted for volatility, trigger the color shift. The larger the ATR (higher volatility), the greater the price movement required to cause a change in color.
Bullish to Bearish (Green to Red): When the candle closes lower than the open, and the price movement exceeds the dynamic threshold based on ATR, the candle color changes from green to red, signaling a potential bearish reversal.
Bearish to Bullish (Red to Green): When the candle closes higher than the open, and the price movement exceeds the ATR-based threshold, the candle color shifts from red to green, signaling a potential bullish reversal.
Key Features:
Dynamic Color Change: The strategy identifies key color changes from bullish to bearish (green to red) and from bearish to bullish (red to green) based on specific thresholds in candle size.
Customizable Timeframe: You can specify a custom trading window to restrict the strategy’s actions to specific hours of the day.
Stop Loss and Take Profit: The strategy incorporates risk management features, allowing you to set a stop loss and take profit based on the price in pips.
Flexible Trade Types: Choose between "Both" (long and short), "Long Only," or "Short Only" trading options to suit your preferred trading style.
Visual Alerts: Receive visual alerts with arrows when color changes occur, signaling potential trade opportunities. Green arrows indicate a bullish shift, while red arrows show a bearish shift.
This strategy is ideal for traders who prefer a color-coded overlay to easily visualize price action and make informed decisions based on bullish or bearish trends. Whether you’re looking for quick, short-term opportunities or analyzing market reversals, this strategy offers an intuitive approach to identifying trade signals.
Buyers vs SellersBuyers vs Sellers is an indicator which essentially weighs the strength of the buyers against the strength of the sellers. It defines the current relationship between the buyers and the sellers as well as the way that that relationship is changing over time.
User Inputs:
1. Number of Bars To Include In The Calculation - this is the look back period. The amount of past data that is being processed.
2. Length of The ATR - higher values are recommended. This ATR is used as a unit in which the price changes are expressed.
3. Bullish/Bearish Bias Threshold - the minimum value to consider the buyers or the sellers having control of the price.
4. Net Move Average Length - the moving average of the sum of bullish and bearish price changes.
The Calculation Process:
This indicator measures the difference between the opening and the closing prices of each bar in the look back period.
After that it sums together the sizes of the bodies of all the bullish bars and also the sizes of all the bearish bars to create the total bullish price change and total bearish price change for the look back period.
After that it converts the total price changes into percentages of the ATR and divides them by the look back period to get the price change per bar - it is a way of getting the price change values down to less ridiculous numbers regardless of the look back period and while still keeping the proportions intact.
After that it sums the two price changes together to get the net move and performs a simple moving average calculation on it in order to smooth out the values. This is a numerical representation of the relationship between the strength of the bullish and the bearish moves, which is easily readable from the chart.
After that the indicator performs a natural logarithm of the bullish price change divided by the bearish price change. This calculation gives a relationship between the two values which is not tied to the volatility of the instrument, but is expressed purely as a relationship between the strength of one value against the other. The idea is that this would allow for easier comparison across different instruments as the same numbers would represent exactly the same distribution of the strength difference.
The Plotting Logic:
The ATR is plotted as just a number as a reference.
The natural logarithm is presented in two ways.
One way is numerical, to be able to precisely read the value and the colour of the number changes depending if it is positive and above the bias threshold or negative and below the bias threshold.
The other way is in the form of a background colour. It only visualises the bias that can be interpreted based on the logarithm value in relation to the set bias threshold.
The total bullish price change and the total bearish price change are both plotted as a line with the fill between that line and the zero line. This helps visualise the bullish and the bearish moves individually.
The moving average of the sum of the bullish and the bearish moves is added as a line to represent the relationship between the two on a graph and not just as a logarithm.
I hope this indicator will serve you well and help with defining the relationship between the buyers and sellers more objectively, hopefully leading to more profitable trades.
Stage Market V4This script provides a comprehensive tool for identifying market stages based on exponential moving averages (EMAs), market performance metrics, and additional price statistics. Below is a summary of its functionality and instructions on how to use it:
1. Inputs and Configuration
Fast and Slow EMA:
Fast EMA Length: Determines the period for the fast EMA.
Slow EMA Length: Determines the period for the slow EMA.
Additional EMAs:
Enable or disable three additional EMAs (EMA 1, EMA 2, and EMA 3) with customizable lengths.
52-Week High Display:
Optionally display the percentage distance from the 52-week high.
2. Market Stages
The indicator identifies six market stages based on the relationship between the price, fast EMA, and slow EMA:
Recovery: Price is above the fast EMA, and the slow EMA is above both the price and the fast EMA.
Accumulation: Price is above both the fast EMA and slow EMA, but the slow EMA is still above the fast EMA.
Bull Market: Price, fast EMA, and slow EMA are all aligned in a rising trend.
Warning: Price is below the fast EMA, but still above the slow EMA, signaling potential weakness.
Distribution: Price is below both EMAs, but the slow EMA remains below the fast EMA.
Bear Market: Price, fast EMA, and slow EMA are all aligned in a falling trend.
The current stage is displayed in a table along with the number of bars spent in that stage.
3. Performance Metrics
The script calculates additional metrics to gauge the stock's performance:
30-Day Change: The percentage price change over the last 30 days.
90-Day Change: The percentage price change over the last 90 days.
Year-to-Date (YTD) Change: The percentage change from the year's first closing price.
Distance from 52-Week High (if enabled): The percentage difference between the current price and the highest price over the past 52 weeks.
These values are color-coded:
Green for positive changes.
Red for negative changes.
4. Table Display
The indicator uses a table in the bottom-right corner of the chart to show:
Current market stage and bars spent in the stage.
30-day, 90-day, and YTD changes.
Distance from the 52-week high (if enabled).
5. EMA Plotting
The script plots the following EMAs on the chart:
Fast EMA (default: 50-period) in yellow.
Slow EMA (default: 200-period) in orange.
Optional EMAs (EMA 1, EMA 2, and EMA 3) in blue, green, and purple, respectively.
6. Using the Indicator
Add the indicator to your chart via the Pine Editor in TradingView.
Customize the input parameters to fit your trading style or the asset's characteristics.
Use the table to quickly assess the current market stage and key performance metrics.
Observe the plotted EMAs to understand trend alignments and potential crossovers.
This script is particularly useful for identifying market trends, understanding price momentum, and aligning trading decisions with broader market conditions.
Heat Map Trend (VIDYA MA) [BigBeluga]The Heat Map Trend (VIDYA MA) - BigBeluga indicator is a multi-timeframe trend detection tool based on the Volumetric Variable Index Dynamic Average (VIDYA). This indicator calculates trends using volume momentum, or volatility if volume data is unavailable, and displays the trends across five customizable timeframes. It features a heat map to visualize trends, color-coded candles based on an average of the five timeframes, and a dashboard that shows the current trend direction for each timeframe. This tool helps traders identify trends while minimizing market noise and is particularly useful in detecting faster market changes in shorter timeframes.
🔵 KEY FEATURES & USAGE
◉ Volumetric Variable Index Dynamic Average (VIDYA):
The core of the indicator is the VIDYA moving average, which adjusts dynamically based on volume momentum. If volume data isn't available, the indicator uses volatility instead to smooth the moving average. This allows traders to assess the trend direction with more accuracy, using either volume or volatility, if volume data is not provided, as the basis for the trend calculation.
// VIDYA CALCULATION -----------------------------------------------------------------------------------------
// ATR (Average True Range) and volume calculation
bool volume_check = ta.cum(volume) <= 0
float atrVal = ta.atr(1)
float volVal = volume_check ? atrVal : volume // Use ATR if volume is not available
// @function: Calculate the VIDYA (Volumetric Variable Index Dynamic Average)
vidya(src, len, cmoLen) =>
float cmoVal = ta.sma(ta.cmo(volVal, cmoLen), 10) // Calculate the CMO and smooth it with an SMA
float absCmo = math.abs(cmoVal) // Absolute value of CMO
float alpha = 2 / (len + 1) // Alpha factor for smoothing
var float vidyaVal = 0.0 // Initialize VIDYA
vidyaVal := alpha * absCmo / 100 * src + (1 - alpha * absCmo / 100) * nz(vidyaVal ) // VIDYA formula
◉ Multi-Timeframe Trend Analysis with Heat Map Visualization:
The indicator calculates VIDYA across five customizable timeframes, allowing traders to analyze trends from multiple perspectives. The resulting trends are displayed as a heat map below the chart, where each timeframe is represented by a gradient color. The color intensity reflects the distance of the moving average (VIDYA) from the price, helping traders to identify trends on different timeframes visually. Shorter timeframes in the heat map are particularly useful for detecting faster market changes, while longer timeframes help to smooth out market noise and highlight the general trend.
Trend Direction:
Heat Map Reading:
◉ Dashboard for Multi-Timeframe Trend Directions:
The built-in dashboard displays the trend direction for each of the five timeframes, showing whether the trend is up or down. This quick overview provides traders with valuable insights into the current market conditions across multiple timeframes, helping them to assess whether the market is aligned or if there are conflicting trends. This allows for more informed decisions, especially during volatile periods.
◉ Color-Coded Candles Based on Multi-Timeframe Averages:
Candles are dynamically colored based on the average of the VIDYA across all five timeframes. When the price is in an uptrend, the candles are colored blue, while in a downtrend, they are colored red. If the VIDYA averages suggest a possible trend shift, the candles are displayed in orange to highlight a potential change in momentum. This color coding simplifies the process of identifying the dominant trend and spotting potential reversals.
BTC:
SP500:
◉ UP and DOWN Signals for Trend Direction Changes:
The indicator provides clear UP and DOWN signals to mark trend direction changes. When the average VIDYA crosses above a certain threshold, an UP signal is plotted, indicating a shift to an uptrend. Conversely, when it crosses below, a DOWN signal is shown, highlighting a transition to a downtrend. These signals help traders to quickly identify shifts in market direction and respond accordingly.
🔵 CUSTOMIZATION
VIDYA Length and Momentum Settings:
Adjust the length of the VIDYA moving average and the period for calculating volume momentum. These settings allow you to fine-tune how sensitive the indicator is to market changes, helping to match it with your preferred trading style.
Timeframe Selection:
Select five different timeframes to analyze trends simultaneously. This gives you the flexibility to focus on short-term trends, long-term trends, or a combination of both depending on your trading strategy.
Candle and Heat Map Color Customization:
Change the colors of the candles and heat map to fit your personal preferences. This customization allows you to align the visuals of the indicator with your overall chart setup, making it easier to analyze market conditions.
🔵 CONCLUSION
The Heat Trend (VIDYA MA) - BigBeluga indicator provides a comprehensive, multi-timeframe view of market trends, using VIDYA moving averages that adapt to volume momentum or volatility. Its heat map visualization, combined with a dashboard of trend directions and color-coded candles, makes it an invaluable tool for traders looking to understand both short-term market fluctuations and longer-term trends. By showing the overall market direction across multiple timeframes, it helps traders avoid market noise and focus on the bigger picture while being alert to faster shifts in shorter timeframes.
MarketStructureLibrary "MarketStructure"
Will draw out the market structure for the disired pivot length. The code is from my indicator "Marker structure" ().
Create(type, length, source, equalPivotsFactor, extendEqualPivotsZones, equalPivotsStyle, equalPivotsColor, alertFrequency)
Call on each bar. Will create a Structure object.
Parameters:
type (int) : the type of the Structure to create. 0 = internal, 1 = swing.
length (int) : The lenghts (left and right) for pivots to use.
source (string) : The source to be used for structural changes ('Close', 'High/low (aggresive)' (low in an uptrend) or 'High/low (passive)' (high in an uptrend)).
equalPivotsFactor (float) : Set how the limits are for an equal pivot. This is a factor of the Average True Length (ATR) of length 14. If a low pivot is considered to be equal if it doesn't break the low pivot (is at a lower value) and is inside the previous low pivot + this limit.
extendEqualPivotsZones (bool) : Set to true if you want the equal pivots zones to be extended.
equalPivotsStyle (string) : Set the style of equal pivot zones.
equalPivotsColor (color) : Set the color of equal pivot zones.
alertFrequency (string)
Returns: The 'structure' object.
Pivot(structure)
Sets the pivots in the structure.
Parameters:
structure (Structure)
Returns: The 'structure' object.
PivotLabels(structure)
Draws labels for the pivots found.
Parameters:
structure (Structure)
Returns: The 'structure' object.
EqualHighOrLow(structure)
Draws the boxsa for equal highs/lows. Also creates labels for the pivots included.
Parameters:
structure (Structure)
Returns: The 'structure' object.
BreakOfStructure(structure)
Will create lines when a break of strycture occures.
Parameters:
structure (Structure)
Returns: The 'structure' object.
ChangeOfCharacter(structure)
Will create lines when a change of character occures.
Parameters:
structure (Structure)
Returns: The 'structure' object.
StructureBreak
Holds drawings for a structure break.
Fields:
Line (series line) : The line object.
Label (series label) : The label object.
Pivot
Holds all the values for a found pivot.
Fields:
Price (series float) : The price of the pivot.
BarIndex (series int) : The bar_index where the pivot occured.
Type (series int) : The type of the pivot (-1 = low, 1 = high).
ChangeOfCharacterBroken (series bool) : Sets to true if a change of character has happened.
BreakOfStructureBroken (series bool) : Sets to true if a break of structure has happened.
Structure
Holds all the values for the market structure.
Fields:
Length (series int) : Define the left and right lengths of the pivots used.
Type (series int) : Set the type of the market structure. Two types can be used, 'internal' and 'swing' (0 = internal, 1 = swing).
Trend (series int) : This will be set internally and can be -1 = downtrend, 1 = uptrend.
Source (series string) : Set the source for structural chandeg. Can be 'Close', 'High/low (aggresive)' (low in an uptrend) or 'High/low (passive)' (high in an uptrend).
EqualPivotsFactor (series float) : Set how the limits are for an equal pivot. This is a factor of the Average True Length (ATR) of length 14. If a low pivot is considered to be equal if it doesn't break the low pivot (is at a lower value) and is inside the previous low pivot + this limit.
ExtendEqualPivotsZones (series bool) : Set to true if you want the equal pivots zones to be extended.
ExtendEqualPivotsStyle (series string) : Set the style of equal pivot zones.
ExtendEqualPivotsColor (series color) : Set the color of equal pivot zones.
EqualHighs (array) : Holds the boxes for zones that contains equal highs.
EqualLows (array) : Holds the boxes for zones that contains equal lows.
BreakOfStructures (array) : Holds all the break of structures within the trend (before a change of character).
Pivots (array) : All the pivots in the current trend, added with the latest first, this is cleared when the trend changes.
AlertFrequency (series string) : set the frequency for alerts.
Candlestick based on volume
This code is an indicator for drawing custom candle charts based on volume and analyzing price fluctuations and trends. A specific description is provided below:
Main functions and analysis details
Cumulative Volume Calculation
Accumulates the volume of all bars and calculates the cumulative volume. This gives an idea of the total volume of volume.
Counter Calculation
The value of the counter is determined by continuously dividing the accumulated volume by 2. This counter shows the change in volume.
Calculation of Counter Change and Duration
When the value of the counter changes, the duration of the change is calculated. This tells us how long the change in volume lasted.
Calculation of slope and angle
The slope is calculated from the amount of change in the counter and the period of time it took for the counter to change, and the angle is calculated from the slope. This allows you to visualize the trend of the volume change and the direction of the trend.
Setting Counter Color and Background Color
Set the color of the counter based on the period of change. Longer periods are displayed in red, and shorter periods in green. The background color also changes based on the angle, indicating the strength and direction of the trend.
Drawing Custom Candles
Draw custom candles based on volume changes. As the counter changes, a new candle is formed, highlighting the price movement.
Display of simple moving averages (SMA)
Calculates the average of prices over a selected period of time and displays that average. This smoothes out price trends and fluctuations and clearly shows the direction of the trend.
Comparison of the upper and lower lengths of candles
Calculates the upper and lower lengths of each candle (lower half and upper half) and changes the color of the SMA based on which is longer. This visualizes the effect of price fluctuations due to the shape of the candles.
Key Points of Use
Trend Analysis: Analyze the direction and strength of a trend using custom candles based on volume, background color, and tilt angle.
Change highlighting: Visually highlight important points with counter changes and flags.
Price Averaging: Use SMA to smooth price trends, reduce noise, and determine trend direction.
WaveTrend With Divs & RSI(STOCH) Divs by WeloTradesWaveTrend with Divergences & RSI(STOCH) Divergences by WeloTrades
Overview
The "WaveTrend With Divergences & RSI(STOCH) Divergences" is an advanced Pine Script™ indicator designed for TradingView, offering a multi-dimensional analysis of market conditions. This script integrates several technical indicators—WaveTrend, Money Flow Index (MFI), RSI, and Stochastic RSI—into a cohesive tool that identifies both regular and hidden divergences across these indicators. These divergences can indicate potential market reversals and provide critical trading opportunities.
This indicator is not just a simple combination of popular tools; it offers extensive customization options, organized data presentation, and valuable trading signals that are easy to interpret. Whether you're a day trader or a long-term investor, this script enhances your ability to make informed decisions.
Originality and Usefulness
The originality of this script lies in its integration and the synergy it creates among the indicators used. Rather than merely combining multiple indicators, this script allows them to work together, enhancing each other's strengths. For example, by identifying divergences across WaveTrend, RSI, and Stochastic RSI simultaneously, the script provides multiple layers of confirmation, which reduces the likelihood of false signals and increases the reliability of trading signals.
The usefulness of this script is apparent in its ability to offer a consolidated view of market dynamics. It not only simplifies the analytical process by combining different indicators but also provides deeper insights through its divergence detection features. This comprehensive approach is designed to help traders identify potential market reversals, confirm trends, and ultimately make more informed trading decisions.
How the Components Work Together
1. Cross-Validation of Signals
WaveTrend: This indicator is primarily used to identify overbought and oversold conditions, as well as potential buy and sell signals. WaveTrend's ability to smooth price data and reduce noise makes it a reliable tool for identifying trend reversals.
RSI & Stochastic RSI: These momentum oscillators are used to measure the speed and change of price movements. While RSI identifies general overbought and oversold conditions, Stochastic RSI offers a more granular view by tracking the RSI’s level relative to its high-low range over a period of time. When these indicators align with WaveTrend signals, it adds a layer of confirmation that enhances the reliability of the signals.
Money Flow Index (MFI): This volume-weighted indicator assesses the inflow and outflow of money in an asset, giving insights into buying and selling pressure. By analyzing the MFI alongside WaveTrend and RSI indicators, the script can cross-validate signals, ensuring that buy or sell signals are supported by actual market volume.
Example Bullish scenario:
When a bullish divergence is detected on the RSI and confirmed by a corresponding bullish signal on the WaveTrend, along with an increasing Money Flow Index, the probability of a successful trade setup increases. This cross-validation minimizes the risk of acting on false signals, which might occur when relying on a single indicator.
Example Bearish scenario:
When a bearish divergence is detected on the RSI and confirmed by a corresponding bearish signal on the WaveTrend, along with an decreasing Money Flow Index, the probability of a successful trade setup increases. This cross-validation minimizes the risk of acting on false signals, which might occur when relying on a single indicator.
2. Divergence Detection and Market Reversals
Regular Divergences: Occur when the price action and an indicator (like RSI or WaveTrend) move in opposite directions. Regular bullish divergence signals a potential upward reversal when the price makes a lower low while the indicator makes a higher low. Conversely, regular bearish divergence suggests a downward reversal when the price makes a higher high, but the indicator makes a lower high.
Hidden Divergences: These occur when the price action and indicator move in the same direction, but with different momentum. Hidden bullish divergence suggests the continuation of an uptrend, while hidden bearish divergence suggests the continuation of a downtrend. By detecting these divergences across multiple indicators, the script identifies potential trend reversals or continuations with greater accuracy.
Example: The script might detect a regular bullish divergence on the WaveTrend while simultaneously identifying a hidden bullish divergence on the RSI. This combination suggests that while a trend reversal is possible, the overall market sentiment remains bullish, providing a nuanced view of the market.
A Regular Bullish Divergence Example:
A Hidden Bullish Divergence Example:
A Regular Bearish Divergence Example:
A Hidden Bearish Divergence Example:
3. Trend Strength and Sentiment Analysis
WaveTrend: Measures the strength and direction of the trend. By identifying the extremes of market sentiment (overbought and oversold levels), WaveTrend provides early signals for potential reversals.
Money Flow Index (MFI): Assesses the underlying sentiment by analyzing the flow of money. A rising MFI during an uptrend confirms strong buying pressure, while a falling MFI during a downtrend confirms selling pressure. This helps traders assess whether a trend is likely to continue or reverse.
RSI & Stochastic RSI: Offer a momentum-based perspective on the trend’s strength. High RSI or Stochastic RSI values indicate that the asset may be overbought, suggesting a potential reversal. Conversely, low values indicate oversold conditions, signaling a possible upward reversal.
Example:
During a strong uptrend, the WaveTrend & RSI's might signal overbought conditions, suggesting caution. If the MFI also shows decreasing buying pressure and the RSI reaches extreme levels, these indicators together suggest that the trend might be weakening, and a reversal could be imminent.
Example:
During a strong downtrend, the WaveTrend & RSI's might signal oversold conditions, suggesting caution. If the MFI also shows increasing buying pressure and the RSI reaches extreme levels, these indicators together suggest that the trend might be weakening, and a reversal could be imminent.
Conclusion
The "WaveTrend With Divergences & RSI(STOCH) Divergences" script offers a powerful, integrated approach to technical analysis by combining trend, momentum, and sentiment indicators into a single tool. Its unique value lies in the cross-validation of signals, the ability to detect divergences, and the comprehensive view it provides of market conditions. By offering traders multiple layers of analysis and customization options, this script is designed to enhance trading decisions, reduce false signals, and provide clearer insights into market dynamics.
WAVETREND
Display of WaveTrend:
Display of WaveTrend Setting:
WaveTrend Indicator Explanation
The WaveTrend indicator helps identify overbought and oversold conditions, as well as potential buy and sell signals. Its flexibility allows traders to adapt it to various strategies, making it a versatile tool in technical analysis.
WaveTrend Input Settings:
WT MA Source: Default: HLC3
What it is: The data source used for calculating the WaveTrend Moving Average.
What it does: Determines the input data to smooth price action and filter noise.
Example: Using HLC3 (average of High, Low, Close) provides a smoother data representation compared to using just the closing price.
Length (WT MA Length): Default: 3
What it is: The period used to calculate the Moving Average.
What it does: Adjusts the sensitivity of the WaveTrend indicator, where shorter lengths respond more quickly to price changes.
Example: A length of 3 is ideal for short-term analysis, providing quick reactions to price movements.
WT Channel Length & Average: Default: WT Channel Length = 9, Average = 12
What it is: Lengths used to calculate the WaveTrend channel and its average.
What it does: Smooths out the WaveTrend further, reducing false signals by averaging over a set period.
Example: Higher values reduce noise and help in identifying more reliable trends.
Channel: Style, Width, and Color:
What it is: Customization options for the WaveTrend channel's appearance.
What it does: Adjusts how the channel is displayed, including line style, width, and color.
Example: Choosing an area style with a distinct color can make the WaveTrend indicator clearly visible on the chart.
WT Buy & Sell Signals:
What it is: Settings to enable and customize buy and sell signals based on WaveTrend.
What it does: Allows for the display of buy/sell signals and customization of their shapes and colors.
When it gives a Buy Signal: Generated when the WaveTrend line crosses below an oversold level and then rises back, indicating a potential upward price movement.
When it gives a Sell Signal: Triggered when the WaveTrend line crosses above an overbought level and then declines, suggesting a possible downward trend.
Example: The script identifies these signals based on mean reversion principles, where prices tend to revert to the mean after reaching extremes. Traders can use these signals to time their entries and exits effectively.
WAVETREND OVERBOUGTH AND OVERSOLD LEVELS
Display of WaveTrend with Overbought & Oversold Levels:
Display of WaveTrend Overbought & Oversold Levels Settings:
WaveTrend Overbought & Oversold Levels Explanation
WT OB & OS Levels: Default: OB Level 1 = 53, OB Level 2 = 60, OS Level 1 = -53, OS Level 2 = -60
What it is: The default overbought and oversold levels used by the WaveTrend indicator to signal potential market reversals.
What it does: When the WaveTrend crosses above the OB levels, it indicates an overbought condition, potentially signaling a reversal or selling opportunity. Conversely, when it crosses below the OS levels, it indicates an oversold condition, potentially signaling a reversal or buying opportunity.
Example: A trader might use these levels to time entry or exit points, such as selling when the WaveTrend crosses into the overbought zone or buying when it crosses into the oversold zone.
Show OB/OS Levels: Default: True
What it is: Toggle options to show or hide the overbought and oversold levels on your chart.
What it does: When enabled, these levels will be visually represented on your chart, helping you to easily identify when the market reaches these critical thresholds.
Example: Displaying these levels can help you quickly see when the WaveTrend is approaching or has crossed into overbought or oversold territory, allowing for more informed trading decisions.
Line Style, Width, and Color for OB/OS Levels:
What it is: Options to customize the appearance of the OB and OS levels on your chart, including line style (solid, dotted, dashed), line width, and color.
What it does: These settings allow you to adjust how prominently these levels are displayed on your chart, which can help you better visualize and respond to overbought or oversold conditions.
Example: Setting a thicker, dashed line in a contrasting color can make these levels stand out more clearly, aiding in quick visual identification.
Example of Use:
Scenario: A trader wants to identify potential selling points when the market is overbought. They set the OB levels at 53 and 60, choosing a solid, red line style to make these levels clear on their chart. As the WaveTrend crosses above 53, they monitor for further price action, and upon crossing 60, they consider initiating a sell order.
WAVETREND DIVERGENCES
Display of WaveTrend Divergence:
Display of WaveTrend Divergence Setting:
WaveTrend Divergence Indicator Explanation
The WaveTrend Divergence feature helps identify potential reversal points in the market by highlighting divergences between the price and the WaveTrend indicator. Divergences can signal a shift in market momentum, indicating a possible trend reversal. This component allows traders to visualize and customize divergence detection on their charts.
WaveTrend Divergence Input Settings:
Potential Reversal Range: Default: 28
What it is: The number of bars to look back when detecting potential tops and bottoms.
What it does: Sets the range for identifying possible reversal points based on historical data.
Example: A setting of 28 looks back across the last 28 bars to find reversal points, offering a balance between responsiveness and reliability.
Reversal Minimum LVL OB & OS: Default: OB = 35, OS = -35
What it is: The minimum overbought and oversold levels required for detecting potential reversals.
What it does: Adjusts the thresholds that trigger a reversal signal based on the WaveTrend indicator.
Example: A higher OB level reduces the sensitivity to overbought conditions, potentially filtering out false reversal signals.
Lookback Bar Left & Right: Default: Left = 10, Right = 1
What it is: The number of bars to the left and right used to confirm a top or bottom.
What it does: Helps determine the position of peaks and troughs in the price action.
Example: A larger left lookback captures more extended price action before the peak, while a smaller right lookback focuses on the immediate past.
Lookback Range Min & Max: Default: Min = 5, Max = 60
What it is: The minimum and maximum range for the lookback period when identifying divergences.
What it does: Fine-tunes the detection of divergences by controlling the range over which the indicator looks back.
Example: A wider range increases the chances of detecting divergences across different market conditions.
R.Div Minimum LVL OB & OS: Default: OB = 53, OS = -53
What it is: The threshold levels for detecting regular divergences.
What it does: Adjusts the sensitivity of the regular divergence detection.
Example: Higher thresholds make the detection more conservative, identifying only stronger divergence signals.
H.Div Minimum LVL OB & OS: Default: OB = 20, OS = -20
What it is: The threshold levels for detecting hidden divergences.
What it does: Similar to regular divergence settings but for hidden divergences, which can indicate potential reversals that are less obvious.
Example: Lower thresholds make the hidden divergence detection more sensitive, capturing subtler market shifts.
Divergence Label Options:
What it is: Options to display and customize labels for regular and hidden divergences.
What it does: Allows users to visually differentiate between regular and hidden divergences using customizable labels and colors.
Example: Using different colors and symbols for regular (R) and hidden (H) divergences makes it easier to interpret signals on the chart.
Text Size and Color:
What it is: Customization options for the size and color of divergence labels.
What it does: Adjusts the readability and visibility of divergence labels on the chart.
Example: Larger text size may be preferred for charts with a lot of data, ensuring divergence labels stand out clearly.
FAST & SLOW MONEY FLOW INDEX
Display of Fast & Slow Money Flow:
Display of Fast & Slow Money Flow Setting:
Fast Money Flow Indicator Explanation
The Fast Money Flow indicator helps traders identify the flow of money into and out of an asset over a shorter time frame. By tracking the volume-weighted average of price movements, it provides insights into buying and selling pressure in the market, which can be crucial for making timely trading decisions.
Fast Money Flow Input Settings:
Fast Money Flow: Length: Default: 9
What it is: The period used for calculating the Fast Money Flow.
What it does: Determines the sensitivity of the Money Flow calculation. A shorter length makes the indicator more responsive to recent price changes, while a longer length provides a smoother signal.
Example: A length of 9 is suitable for traders looking to capture quick shifts in market sentiment over a short period.
Fast MFI Area Multiplier: Default: 5
What it is: A multiplier applied to the Money Flow area calculation.
What it does: Adjusts the size of the Money Flow area on the chart, effectively amplifying or reducing the visual impact of the indicator.
Example: A higher multiplier can make the Money Flow more prominent on the chart, aiding in the quick identification of significant money flow changes.
Y Position (Y Pos): Default: 0
What it is: The vertical position adjustment for the Fast Money Flow plot on the chart.
What it does: Allows you to move the Money Flow plot up or down on the chart to avoid overlap with other indicators.
Example: Adjusting the Y Position can be useful if you have multiple indicators on the chart and need to maintain clarity.
Fast MFI Style, Width, and Color:
What it is: Customization options for how the Fast Money Flow is displayed on the chart.
What it does: Enables you to choose between different plot styles (line or area), set the line width, and select colors for positive and negative money flow.
Example: Using different colors for positive (green) and negative (red) money flow helps to visually distinguish between periods of buying and selling pressure.
Slow Money Flow Indicator Explanation
The Slow Money Flow indicator tracks the flow of money into and out of an asset over a longer time frame. It provides a broader perspective on market sentiment, smoothing out short-term fluctuations and highlighting longer-term trends.
Slow Money Flow Input Settings:
Slow Money Flow: Length: Default: 12
What it is: The period used for calculating the Slow Money Flow.
What it does: A longer period smooths out short-term fluctuations, providing a clearer view of the overall money flow trend.
Example: A length of 12 is often used by traders looking to identify sustained trends rather than short-term volatility.
Slow MFI Area Multiplier: Default: 5
What it is: A multiplier applied to the Slow Money Flow area calculation.
What it does: Adjusts the size of the Money Flow area on the chart, helping to emphasize the indicator’s significance.
Example: Increasing the multiplier can help highlight the Money Flow in markets with less volatile price action.
Y Position (Y Pos): Default: 0
What it is: The vertical position adjustment for the Slow Money Flow plot on the chart.
What it does: Allows for vertical repositioning of the Money Flow plot to maintain chart clarity when used with other indicators.
Example: Adjusting the Y Position ensures that the Slow Money Flow indicator does not overlap with other key indicators on the chart.
Slow MFI Style, Width, and Color:
What it is: Customization options for the visual display of the Slow Money Flow on the chart.
What it does: Allows you to choose the plot style (line or area), set the line width, and select colors to differentiate positive and negative money flow.
Example: Customizing the colors for the Slow Money Flow allows traders to quickly distinguish between buying and selling trends in the market.
RSI
Display of RSI:
Display of RSI Setting:
RSI Indicator Explanation
The Relative Strength Index (RSI) is a momentum oscillator that measures the speed and change of price movements. It is typically used to identify overbought or oversold conditions in the market, providing traders with potential signals for buying or selling.
RSI Input Settings:
RSI Source: Default: Close
What it is: The data source used for calculating the RSI.
What it does: Determines which price data (e.g., close, open) is used in the RSI calculation, affecting how the indicator reflects market conditions.
Example: Using the closing price is standard practice, as it reflects the final agreed-upon price for a given time period.
MA Type (Moving Average Type): Default: SMA
What it is: The type of moving average applied to the RSI for smoothing purposes.
What it does: Changes the smoothing technique of the RSI, impacting how quickly the indicator responds to price movements.
Example: Using an Exponential Moving Average (EMA) will make the RSI more sensitive to recent price changes compared to a Simple Moving Average (SMA).
RSI Length: Default: 14
What it is: The period over which the RSI is calculated.
What it does: Adjusts the sensitivity of the RSI. A shorter length (e.g., 7) makes the RSI more responsive to recent price changes, while a longer length (e.g., 21) smooths out the indicator, reducing the number of signals.
Example: A 14-period RSI is commonly used for identifying overbought and oversold conditions, providing a balance between sensitivity and reliability.
RSI Plot Style, Width, and Color:
What it is: Options to customize the appearance of the RSI line on the chart.
What it does: Allows you to adjust the visual representation of the RSI, including the line width and color.
Example: Setting a thicker line width and a bright color like yellow can make the RSI more visible on the chart, aiding in quick analysis.
Display of RSI with RSI Moving Average:
RSI Moving Average Explanation
The RSI Moving Average adds a smoothing layer to the RSI, helping to filter out noise and provide clearer signals. It is particularly useful for confirming trend strength and identifying potential reversals.
RSI Moving Average Input Settings:
MA Length: Default: 14
What it is: The period over which the Moving Average is calculated on the RSI.
What it does: Adjusts the smoothing of the RSI, helping to reduce false signals and provide a clearer trend indication.
Example: A 14-period moving average on the RSI can smooth out short-term fluctuations, making it easier to spot genuine overbought or oversold conditions.
MA Plot Style, Width, and Color:
What it is: Customization options for how the RSI Moving Average is displayed on the chart.
What it does: Allows you to adjust the line width and color, helping to differentiate the Moving Average from the main RSI line.
Example: Using a contrasting color for the RSI Moving Average (e.g., magenta) can help it stand out against the main RSI line, making it easier to interpret the indicator.
STOCHASTIC RSI
Display of Stochastic RSI:
Display of Stochastic RSI Setting:
Stochastic RSI Indicator Explanation
The Stochastic RSI (Stoch RSI) is a momentum oscillator that measures the level of the RSI relative to its high-low range over a set period of time. It is used to identify overbought and oversold conditions, providing potential buy and sell signals based on momentum shifts.
Stochastic RSI Input Settings:
Stochastic RSI Length: Default: 14
What it is: The period over which the Stochastic RSI is calculated.
What it does: Adjusts the sensitivity of the Stochastic RSI. A shorter length makes the indicator more responsive to recent price changes, while a longer length smooths out the fluctuations, reducing noise.
Example: A length of 14 is commonly used to identify momentum shifts over a medium-term period, providing a balanced view of potential overbought or oversold conditions.
Display of Stochastic RSI %K Line:
Stochastic RSI %K Line Explanation
The %K line in the Stochastic RSI is the main line that tracks the momentum of the RSI over the chosen period. It is the faster-moving component of the Stochastic RSI, often used to identify entry and exit points.
Stochastic RSI %K Input Settings:
%K Length: Default: 3
What it is: The period used for smoothing the %K line of the Stochastic RSI.
What it does: Smoothing the %K line helps reduce noise and provides a clearer signal for potential market reversals.
Example: A smoothing length of 3 is common, offering a balance between responsiveness and noise reduction, making it easier to spot significant momentum shifts.
%K Plot Style, Width, and Color:
What it is: Customization options for the visual representation of the %K line.
What it does: Allows you to adjust the appearance of the %K line on the chart, including line width and color, to fit your visual preferences.
Example: Setting a blue color and a medium width for the %K line makes it stand out clearly on the chart, helping to identify key points of momentum change.
%K Fill Color (Above):
What it is: The fill color that appears above the %K line on the chart.
What it does: Adds visual clarity by shading the area above the %K line, making it easier to interpret the direction and strength of momentum.
Example: Using a light blue fill color above the %K line can help emphasize bullish momentum, making it visually prominent.
Display of Stochastic RSI %D Line:
Stochastic RSI %D Line Explanation
The %D line in the Stochastic RSI is a moving average of the %K line and acts as a signal line. It is slower-moving compared to the %K line and is often used to confirm signals or identify potential reversals when it crosses the %K line.
Stochastic RSI %D Input Settings:
%D Length: Default: 3
What it is: The period used for smoothing the %D line of the Stochastic RSI.
What it does: Smooths out the %D line, making it less sensitive to short-term fluctuations and more reliable for identifying significant market signals.
Example: A length of 3 is often used to provide a smoothed signal line that can help confirm trends or reversals indicated by the %K line.
%D Plot Style, Width, and Color:
What it is: Customization options for the visual representation of the %D line.
What it does: Allows you to adjust the appearance of the %D line on the chart, including line width and color, to match your preferences.
Example: Setting an orange color and a thicker line width for the %D line can help differentiate it from the %K line, making crossover points easier to spot.
%D Fill Color (Below):
What it is: The fill color that appears below the %D line on the chart.
What it does: Adds visual clarity by shading the area below the %D line, making it easier to interpret bearish momentum.
Example: Using a light orange fill color below the %D line can highlight bearish conditions, making it visually easier to identify.
RSI & STOCHASTIC RSI OVERBOUGHT AND OVERSOLD LEVELS
Display of RSI & Stochastic with Overbought & Oversold Levels:
Display of RSI & Stochastic Overbought & Oversold Settings:
RSI & Stochastic Overbought & Oversold Levels Explanation
The Overbought (OB) and Oversold (OS) levels for RSI and Stochastic RSI indicators are key thresholds that help traders identify potential reversal points in the market. These levels are used to determine when an asset is likely overbought or oversold, which can signal a potential trend reversal.
RSI & Stochastic Overbought & Oversold Input Settings:
RSI & Stochastic Level 1 Overbought (OB) & Oversold (OS): Default: OB Level = 170, OS Level = 130
What it is: The first set of thresholds for determining overbought and oversold conditions for both RSI and Stochastic RSI indicators.
What it does: When the RSI or Stochastic RSI crosses above the overbought level, it suggests that the asset might be overbought, potentially signaling a sell opportunity. Conversely, when these indicators drop below the oversold level, it suggests the asset might be oversold, potentially signaling a buy opportunity.
Example: If the RSI crosses above 170, traders might look for signs of a potential trend reversal to the downside, while a cross below 130 might indicate a reversal to the upside.
RSI & Stochastic Level 2 Overbought (OB) & Oversold (OS): Default: OB Level = 180, OS Level = 120
What it is: The second set of thresholds for determining overbought and oversold conditions for both RSI and Stochastic RSI indicators.
What it does: These levels provide an additional set of reference points, allowing traders to differentiate between varying degrees of overbought and oversold conditions, potentially leading to more refined trading decisions.
Example: When the RSI crosses above 180, it might indicate an extreme overbought condition, which could be a stronger signal for a sell, while a cross below 120 might indicate an extreme oversold condition, which could be a stronger signal for a buy.
RSI & Stochastic Overbought (OB) Band Customization:
OB Level 1: Width, Style, and Color:
What it is: Customization options for the visual appearance of the first overbought band on the chart.
What it does: Allows you to set the line width, style (solid, dotted, dashed), and color for the first overbought band, enhancing its visibility on the chart.
Example: A dashed red line with medium width can clearly indicate the first overbought level, helping traders quickly identify when this threshold is crossed.
OB Level 2: Width, Style, and Color:
What it is: Customization options for the visual appearance of the second overbought band on the chart.
What it does: Allows you to set the line width, style, and color for the second overbought band, providing a clear distinction from the first band.
Example: A dashed red line with a slightly thicker width can represent a more significant overbought level, making it easier to differentiate from the first level.
RSI & Stochastic Oversold (OS) Band Customization:
OS Level 1: Width, Style, and Color:
What it is: Customization options for the visual appearance of the first oversold band on the chart.
What it does: Allows you to set the line width, style (solid, dotted, dashed), and color for the first oversold band, making it visually prominent.
Example: A dashed green line with medium width can highlight the first oversold level, helping traders identify potential buying opportunities.
OS Level 2: Width, Style, and Color:
What it is: Customization options for the visual appearance of the second oversold band on the chart.
What it does: Allows you to set the line width, style, and color for the second oversold band, providing an additional visual cue for extreme oversold conditions.
Example: A dashed green line with a thicker width can represent a more significant oversold level, offering a stronger visual cue for potential buying opportunities.
RSI DIVERGENCES
Display of RSI Divergence Labels:
Display of RSI Divergence Settings:
RSI Divergence Lookback Explanation
The RSI Divergence settings allow traders to customize the parameters for detecting divergences between the RSI (Relative Strength Index) and price action. Divergences occur when the price moves in the opposite direction to the RSI, potentially signaling a trend reversal. These settings help refine the accuracy of divergence detection by adjusting the lookback period and range. ( NOTE: This setting only imply to the RSI. This doesn't effect the STOCHASTIC RSI. )
RSI Divergence Lookback Input Settings:
Lookback Left: Default: 10
What it is: The number of bars to look back from the current bar to detect a potential divergence.
What it does: Defines the left-side lookback period for identifying pivot points in the RSI, which are used to spot divergences. A longer lookback period may capture more significant trends but could also miss shorter-term divergences.
Example: A setting of 10 bars means the script will consider pivot points up to 10 bars before the current bar to check for divergence patterns.
Lookback Right: Default: 1
What it is: The number of bars to look forward from the current bar to complete the divergence pattern.
What it does: Defines the right-side lookback period for confirming a potential divergence. This setting helps ensure that the identified divergence is valid by allowing the script to check subsequent bars for confirmation.
Example: A setting of 1 bar means the script will look at the next bar to confirm the divergence pattern, ensuring that the signal is reliable.
Lookback Range Min: Default: 5
What it is: The minimum range of bars required to detect a valid divergence.
What it does: Sets a lower bound on the range of bars considered for divergence detection. A lower minimum range might capture more frequent but possibly less significant divergences.
Example: Setting the minimum range to 5 ensures that only divergences spanning at least 5 bars are considered, filtering out very short-term patterns.
Lookback Range Max: Default: 60
What it is: The maximum range of bars within which a divergence can be detected.
What it does: Sets an upper bound on the range of bars considered for divergence detection. A larger maximum range might capture more significant divergences but could also include less relevant long-term patterns.
Example: Setting the maximum range to 60 bars allows the script to detect divergences over a longer timeframe, capturing more extended divergence patterns that could indicate major trend reversals.
RSI Divergence Explanation
RSI divergences occur when the RSI indicator and price action move in opposite directions, signaling potential trend reversals. This section of the settings allows traders to customize the appearance and detection of both regular and hidden bullish and bearish divergences.
RSI Divergence Input Settings:
R. Bullish Div Label: Default: True
What it is: An option to display labels for regular bullish divergences.
What it does: Enables or disables the visibility of labels that mark regular bullish divergences, where the price makes a lower low while the RSI makes a higher low, indicating a potential upward reversal.
Example: A trader might use this to spot buying opportunities in a downtrend when a bullish divergence suggests the trend may be reversing.
Bullish Label Color, Line Width, and Line Color:
What it is: Settings to customize the appearance of regular bullish divergence labels.
What it does: Allows you to choose the color of the labels, adjust the width of the divergence lines, and select the color for these lines.
Example: Selecting a green label color and a distinct line width makes bullish divergences easily recognizable on your chart.
R. Bearish Div Label: Default: True
What it is: An option to display labels for regular bearish divergences.
What it does: Enables or disables the visibility of labels that mark regular bearish divergences, where the price makes a higher high while the RSI makes a lower high, indicating a potential downward reversal.
Example: A trader might use this to spot selling opportunities in an uptrend when a bearish divergence suggests the trend may be reversing.
Bearish Label Color, Line Width, and Line Color:
What it is: Settings to customize the appearance of regular bearish divergence labels.
What it does: Allows you to choose the color of the labels, adjust the width of the divergence lines, and select the color for these lines.
Example: Choosing a red label color and a specific line width makes bearish divergences clearly stand out on your chart.
H. Bullish Div Label: Default: False
What it is: An option to display labels for hidden bullish divergences.
What it does: Enables or disables the visibility of labels that mark hidden bullish divergences, where the price makes a higher low while the RSI makes a lower low, indicating potential continuation of an uptrend.
Example: A trader might use this to confirm an existing uptrend when a hidden bullish divergence signals continued buying strength.
Hidden Bullish Label Color, Line Width, and Line Color:
What it is: Settings to customize the appearance of hidden bullish divergence labels.
What it does: Allows you to choose the color of the labels, adjust the width of the divergence lines, and select the color for these lines.
Example: A softer green color with a thinner line width might be chosen to subtly indicate hidden bullish divergences, keeping the chart clean while providing useful information.
H. Bearish Div Label: Default: False
What it is: An option to display labels for hidden bearish divergences.
What it does: Enables or disables the visibility of labels that mark hidden bearish divergences, where the price makes a lower high while the RSI makes a higher high, indicating potential continuation of a downtrend.
Example: A trader might use this to confirm an existing downtrend when a hidden bearish divergence signals continued selling pressure.
Hidden Bearish Label Color, Line Width, and Line Color:
What it is: Settings to customize the appearance of hidden bearish divergence labels.
What it does: Allows you to choose the color of the labels, adjust the width of the divergence lines, and select the color for these lines.
Example: A muted red color with a thinner line width might be selected to indicate hidden bearish divergences without overwhelming the chart.
Divergence Text Size and Color: Default: S (Small)
What it is: Settings to adjust the size and color of text labels for RSI divergences.
What it does: Allows you to customize the size and color of text labels that display the divergence information on the chart.
Example: Choosing a small text size with a bright white color can make divergence labels easily readable without taking up too much space on the chart.
STOCHASTIC DIVERGENCES
Display of Stochastic RSI Divergence Labels:
Display of Stochastic RSI Divergence Settings:
Stochastic RSI Divergence Explanation
Stochastic RSI divergences occur when the Stochastic RSI indicator and price action move in opposite directions, signaling potential trend reversals. These settings allow traders to customize the detection and visual representation of both regular and hidden bullish and bearish divergences in the Stochastic RSI.
Stochastic RSI Divergence Input Settings:
R. Bullish Div Label: Default: True
What it is: An option to display labels for regular bullish divergences in the Stochastic RSI.
What it does: Enables or disables the visibility of labels that mark regular bullish divergences, where the price makes a lower low while the Stochastic RSI makes a higher low, indicating a potential upward reversal.
Example: A trader might use this to spot buying opportunities in a downtrend when a bullish divergence in the Stochastic RSI suggests the trend may be reversing.
Bullish Label Color, Line Width, and Line Color:
What it is: Settings to customize the appearance of regular bullish divergence labels in the Stochastic RSI.
What it does: Allows you to choose the color of the labels, adjust the width of the divergence lines, and select the color for these lines.
Example: Selecting a blue label color and a distinct line width makes bullish divergences in the Stochastic RSI easily recognizable on your chart.
R. Bearish Div Label: Default: True
What it is: An option to display labels for regular bearish divergences in the Stochastic RSI.
What it does: Enables or disables the visibility of labels that mark regular bearish divergences, where the price makes a higher high while the Stochastic RSI makes a lower high, indicating a potential downward reversal.
Example: A trader might use this to spot selling opportunities in an uptrend when a bearish divergence in the Stochastic RSI suggests the trend may be reversing.
Bearish Label Color, Line Width, and Line Color:
What it is: Settings to customize the appearance of regular bearish divergence labels in the Stochastic RSI.
What it does: Allows you to choose the color of the labels, adjust the width of the divergence lines, and select the color for these lines.
Example: Choosing an orange label color and a specific line width makes bearish divergences in the Stochastic RSI clearly stand out on your chart.
H. Bullish Div Label: Default: False
What it is: An option to display labels for hidden bullish divergences in the Stochastic RSI.
What it does: Enables or disables the visibility of labels that mark hidden bullish divergences, where the price makes a higher low while the Stochastic RSI makes a lower low, indicating potential continuation of an uptrend.
Example: A trader might use this to confirm an existing uptrend when a hidden bullish divergence in the Stochastic RSI signals continued buying strength.
Hidden Bullish Label Color, Line Width, and Line Color:
What it is: Settings to customize the appearance of hidden bullish divergence labels in the Stochastic RSI.
What it does: Allows you to choose the color of the labels, adjust the width of the divergence lines, and select the color for these lines.
Example: A softer blue color with a thinner line width might be chosen to subtly indicate hidden bullish divergences, keeping the chart clean while providing useful information.
H. Bearish Div Label: Default: False
What it is: An option to display labels for hidden bearish divergences in the Stochastic RSI.
What it does: Enables or disables the visibility of labels that mark hidden bearish divergences, where the price makes a lower high while the Stochastic RSI makes a higher high, indicating potential continuation of a downtrend.
Example: A trader might use this to confirm an existing downtrend when a hidden bearish divergence in the Stochastic RSI signals continued selling pressure.
Hidden Bearish Label Color, Line Width, and Line Color:
What it is: Settings to customize the appearance of hidden bearish divergence labels in the Stochastic RSI.
What it does: Allows you to choose the color of the labels, adjust the width of the divergence lines, and select the color for these lines.
Example: A muted orange color with a thinner line width might be selected to indicate hidden bearish divergences without overwhelming the chart.
Divergence Text Size and Color: Default: S (Small)
What it is: Settings to adjust the size and color of text labels for Stochastic RSI divergences.
What it does: Allows you to customize the size and color of text labels that display the divergence information on the chart.
Example: Choosing a small text size with a bright white color can make divergence labels easily readable without taking up too much space on the chart.
Alert System:
Custom Alerts for Divergences and Reversals:
What it is: The script includes customizable alert conditions to notify you of detected divergences or potential reversals based on WaveTrend, RSI, and Stochastic RSI.
What it does: Helps you stay informed of key market movements without constantly monitoring the charts, enabling timely decisions.
Example: Setting an alert for regular bearish divergence on the WaveTrend could notify you of a potential sell opportunity as soon as it is detected.
How to Use Alerts:
Set up custom alerts in TradingView based on these conditions to be notified of potential trading opportunities. Alerts are triggered when the indicator detects conditions that match the selected criteria, such as divergences or potential reversals.
By following the detailed guidelines and examples above, you can effectively use and customize this powerful indicator to suit your trading strategy.
For further understanding and customization, refer to the input settings within the script and adjust them to match your trading style and preferences.
How Components Work Together
Synergy and Cross-Validation: The indicator combines multiple layers of analysis to validate trading signals. For example, a WaveTrend buy signal that coincides with a bullish divergence in RSI and positive fast money flow is likely to be more reliable than any single indicator’s signal. This cross-validation reduces the likelihood of false signals and enhances decision-making.
Comprehensive Market Analysis: Each component plays a role in analyzing different aspects of the market. WaveTrend focuses on trend strength, Money Flow indicators assess market sentiment, while RSI and Stochastic RSI offer detailed views of price momentum and potential reversals.
Ideal For
Traders who require a reliable, multifaceted tool for detecting market trends and reversals.
Investors seeking a deeper understanding of market dynamics across different timeframes and conditions, whether in forex, equities, or cryptocurrency markets.
This script is designed to provide a comprehensive tool for technical analysis, combining multiple indicators and divergence detection into one versatile and customizable script. It is especially useful for traders who want to monitor various indicators simultaneously and look for convergence or divergence signals across different technical tools.
Acknowledgements
Special thanks to these amazing creators for inspiration and their creations:
I want to thank these amazing creators for creating there amazing indicators , that inspired me and also gave me a head start by making this indicator! Without their amazing indicators it wouldn't be possible!
vumanchu: VuManChu Cipher B Divergences.
MisterMoTa: RSI + Divergences + Alerts .
DevLucem: Plain Stochastic Divergence.
Note
This indicator is designed to be a powerful tool in your trading arsenal. However , it is essential to backtest and adjust the settings according to your trading strategy before applying it to live trading . If you have any questions or need further assistance, feel free to reach out.
Modern Trend IdentifierThis is an update by Lightangel112 to Trendilo (Open-Source).
Thanks @ Lightangel112
The Modern Trend Identifier (MTI) is a sophisticated technical analysis tool designed for traders and analysts seeking to accurately determine market trends. This indicator leverages the Arnaud Legoux Moving Average (ALMA) to smooth price data and calculate percentage changes, providing a clearer and more responsive trend analysis. MTI is engineered to highlight trend direction with visual cues, fill areas between the indicator and its bands, and color bars based on trend direction, making it a powerful tool for identifying market momentum and potential reversals.
Capabilities
Smoothing and Trend Calculation:
Utilizes ALMA to smooth price data, reducing noise and providing a clearer view of the trend.
Calculates percentage changes in price over a user-defined lookback period.
Dynamic Range Adjustment:
Normalizes the ALMA percentage change values to ensure they stay within a -100 to 100 range.
Uses a combination of linear and smoothstep compression to handle extreme values without losing sensitivity.
Trend Direction and Highlighting:
Determines the trend direction based on the relationship between the smoothed ALMA percentage change and dynamically adjusted RMS (Root Mean Square) bands.
Colors the trend line to visually indicate whether the market is in an uptrend, downtrend, or neutral state.
Dynamic Threshold Calculation:
Calculates dynamic thresholds using percentile ranks to adapt to changing market conditions.
Visualization Enhancements:
Fills areas between the ALMA percentage change line and its RMS bands to provide a clear visual indication of the trend strength.
Offers the option to color price bars based on the identified trend direction.
Customizable Settings:
Provides extensive customization options for lookback periods, smoothing parameters, ALMA settings, band multipliers, and more.
Allows users to enable or disable various visual enhancements and customize their appearance.
Use Cases
Trend Identification:
MTI helps traders identify the current market trend, whether it's bullish, bearish, or neutral. This can be particularly useful for trend-following strategies.
Momentum Analysis:
By highlighting areas of strong momentum, MTI enables traders to spot potential breakouts or breakdowns. This can be useful for both entry and exit decisions.
Support and Resistance Levels:
The dynamic threshold bands can act as support and resistance levels. Traders can use these levels to set stop-loss and take-profit orders.
Divergence Detection:
MTI can help in identifying divergences between price and the indicator, which can signal potential trend reversals. This is useful for traders looking to capitalize on trend changes.
Risk Management:
The fill areas and colored bars provide clear visual cues about trend strength and direction, aiding in better risk management. Traders can adjust their positions based on the strength of the trend.
Backtesting:
The extensive customization options allow traders to backtest different settings and parameters to optimize their trading strategies for various market conditions.
Multiple Timeframes:
MTI can be applied to multiple timeframes, from intraday charts to daily, weekly, or monthly charts, making it a versatile tool for traders with different trading styles.
Example Scenarios
Day Trading:
A day trader can use MTI on a 5-minute chart to identify intraday trends. By adjusting the lookback period and smoothing parameters, the trader can quickly spot potential entry and exit points based on short-term momentum changes.
Swing Trading:
A swing trader might apply MTI to a 4-hour chart to identify medium-term trends. The dynamic thresholds can help in setting appropriate stop-loss levels, while the trend direction highlighting aids in making informed decisions about holding or exiting positions.
Position Trading:
For a position trader using a daily chart, MTI can help identify the overarching trend. The trader can use the fill areas and bar coloring to assess the strength of the trend and make decisions about entering or exiting long-term positions.
Market Analysis:
An analyst could use MTI to study historical price movements and identify patterns. By examining how the indicator reacted to past market conditions, the analyst can gain insights into potential future price movements.
In summary, the Modern Trend Identifier (MTI) is a versatile and powerful tool that enhances trend analysis with advanced smoothing techniques, dynamic adjustments, and comprehensive visual cues. It is designed to meet the needs of traders and analysts across various trading styles and timeframes, providing clear and actionable insights into market trends and momentum.
Updated with the following:
Additions and Enhancements in MTI
Grouped Inputs with Descriptive Tooltips:
Inputs are organized into groups for better clarity.
Each input parameter includes a descriptive tooltip.
Dynamic Threshold Calculation:
Added dynamic threshold calculation using percentile ranks to adapt to changing market conditions.
Normalization and Compression:
Added normalization factor to ensure plots are within -100 to 100 range.
Introduced smoothstep function for smooth transition and selectively applied linear and smoothstep compression to values outside -80 to 100 range.
Enhanced Visualization:
Highlighted trend direction with RGB colors.
Enhanced fill areas between the ALMA percentage change line and its RMS bands.
Colored price bars based on the identified trend direction.
RMS Lines Adjustment:
Dynamically adjusted RMS calculation without strict capping.
Ensured RMS lines stay below fill areas to maintain clarity.
Descriptive and Organized Code:
Enhanced code clarity with detailed comments.
Organized code into logical sections for better readability and maintenance.
Key Differences and Improvements.
Input Customization:
Trendilo: Inputs are simple and ungrouped.
MTI: Inputs are grouped and include tooltips for better user guidance.
Trend Calculation:
Trendilo: Uses ALMA and calculates percentage change.
MTI: Enhanced with normalization, compression, and dynamic threshold calculation.
Normalization and Compression:
Trendilo: No normalization or compression applied.
MTI: Normalizes values to -100 to 100 range and applies smoothstep compression to handle extreme values.
Dynamic RMS Adjustment:
Trendilo: Simple RMS calculation.
MTI: Dynamically adjusted RMS calculation to ensure clarity in visualization.
Visual Enhancements:
Trendilo: Basic trend highlighting and filling.
MTI: Enhanced visual cues with RGB colors, dynamic threshold bands, and improved fill areas.
Code Clarity:
Trendilo: Functional but lacks detailed comments and organization.
MTI: Well-organized, extensively commented code for better readability and maintainability.
Session Markers - JDK AnalysisSession Markers is a tool designed to study how markets behave during specific, recurring time windows. Many traders know that price behaves differently depending on the day of the week, the time of the day, or particular market sessions such as the weekly open, the London session, or the New York open. This indicator makes those recurring windows visible on the chart and then analyzes what price typically does inside them. The result is a clear statistical understanding of how a chosen session behaves, both in direction and in strength.
The script works by allowing the trader to define any time window using a start day and time and an end day and time. Every time this window occurs on the chart, the indicator highlights it with a full-height vertical band. These visual markers reveal patterns that are otherwise difficult to detect manually, such as whether certain sessions tend to trend, reverse, consolidate, or create large imbalances. They also help the trader quickly scan through historical price action to see how the market has behaved under similar conditions.
For every completed session window, the indicator measures how much price changed from the moment the window began to the moment it ended. Instead of using raw price differences, it converts these changes into percentage moves. This makes the measurement consistent across different price ranges and market regimes. A one-percent move always has the same meaning, whether the asset is trading at 100 or 50,000. These percentage moves are collected for a user-selected number of past sessions, creating a dataset of how the market has behaved in the chosen time window.
Based on this dataset, the indicator generates several statistics. It counts how many past sessions closed higher and how many closed lower, producing a directional tendency. It also computes the probability of an upward session by dividing the number of positive sessions by the total. More importantly, it calculates the average percentage movement for all sessions in the lookback period. This average move reflects not just the direction but also the magnitude of price changes. A session with frequent small upward moves but occasional large downward moves will show a negative average movement, even if more sessions ended positive. This creates a more realistic representation of true market behavior.
Using this average movement, the script determines a “Bias” for the session. If the average percentage move is positive, the bias is considered bullish. If it is negative, the bias is bearish. If the values are very close to zero, the bias is neutral. This way, the indicator takes both frequency and impact into account, producing a magnitude-aware assessment instead of one that only counts wins and losses. A sequence such as +5%, –1% results in a bullish bias because the overall impact is strongly positive. On the other hand, a series of small gains followed by a large drop produces a bearish bias even if more sessions ended positive, because the large move dominates the average. This provides a far more truthful picture of what the market tends to do during the chosen window.
All relevant statistics are displayed neatly in a small panel in the top-right corner of the chart. The panel updates in real time as new sessions complete and older ones fall out of the lookback range. It shows how many sessions were analyzed, how many ended up or down, the probability of an upward move, the average percentage change, and the final bias. The background color of the panel instantly reflects that bias, making it easy to interpret at a glance.
To use the tool effectively, the trader simply needs to define a time window of interest. This could be something like the weekly opening window from Sunday to Monday, the London open each day, or even a unique custom window. After selecting how many past sessions to analyze, the indicator takes care of the rest. The vertical session markers reveal the structure visually. The statistics summarize the historical behavior objectively. The magnitude-weighted bias provides a realistic indication of whether the window tends to produce upward or downward movement on average.
Session Markers is helpful because it translates repeated market timing behavior into measurable data. It exposes hidden tendencies that are easy to feel intuitively but hard to quantify manually. By analyzing both direction and magnitude, it prevents misleading interpretations that can arise from looking only at win rates. It helps traders understand whether a session typically produces meaningful moves or just small noise, whether it tends to trend or reverse, and whether its behavior has recently changed. Whether used for bias building, session filtering, or deeper market research, it offers a structured framework for understanding the market through time-based patterns.
Ichimoku Average with Margin█ OVERVIEW
“Ichimoku Average with Margin” is a technical analysis indicator based on an average of selected Ichimoku system lines, enhanced with a dynamic safety margin (tolerance). Designed for traders seeking a simple yet effective tool for trend identification with breakout confirmation. The indicator offers flexible settings, line and label coloring, visual fills, and alerts for trend changes.
█ CONCEPT
The Ichimoku Cloud (Ichimoku Kinko Hyo) is an excellent, comprehensive technical analysis system, but for many traders—especially beginners—it remains difficult to interpret due to multiple overlapping lines and time displacements.
Experimentally, I decided to create a simplified version based on its foundations: combining selected lines into a single readable average (avgLine) and introducing a dynamic safety margin that acts as a buffer against market noise.
This is not the full Ichimoku system—it’s merely a clear method for determining trend, accessible even to beginners. The trend changes only after the price closes beyond the margin, eliminating false signals.
█ FEATURES
Ichimoku Lines:
- Tenkan-sen (Conversion Line) – Donchian average over 9 periods
- Kijun-sen (Base Line) – Donchian average over 26 periods
- Senkou Span A – average of Tenkan and Kijun
- Senkou Span B – Donchian average over 52 periods
- Chikou Span – close price (no offset)
Dynamic Average (avgLine):
- Arithmetic mean of only the enabled Ichimoku lines – full component selection flexibility.
Safety Margin (tolerance):
Calculated as:
- tolerance = multiplier × SMA(|open - close|, periods)
- Default: multiplier 1.8, period 100.
Trend Detection:
- Uptrend → when price > avgLine + tolerance
- Downtrend → when price < avgLine - tolerance
- Trend changes only after full margin breakout.
- Margin can be set to 0 – then signals trigger on avgLine crossover.
Signal Labels:
- “Buy” (green, upward arrow) – on shift to uptrend
- “Sell” (red, downward arrow) – on shift to downtrend
Visual Fills:
- Between avgLine and marginLine
- Between avgLine and price (with transparency)
- Colors: green (uptrend), red (downtrend)
Alerts:
- Trend Change Up – price crosses above margin
- Trend Change Down – price crosses below margin
█ HOW TO USE
Add to Chart: Paste code in Pine Editor or find in the indicator library.
Settings:
Ichimoku Parameters:
- Conversion Line Length → default 9
- Base Line Length → default 26
- Leading Span B Length → default 52
- Average Body Periods → default 100
- Tolerance Multiplier → default 1.8
Line Selection:
- Enable/disable: Tenkan, Kijun, Span A, Span B, Chikou
Visual Settings:
- Uptrend Color → default green
- Downtrend Color → default red
- Fill Between Price & Avg → enables shadow fill
Signal Interpretation:
- Average Line (avgLine): Primary trend reference level.
- Margin (marginLine): Buffer – price must break it to change trend. Set to 0 for signals on avgLine crossover.
- Buy/Sell Labels: Appear only on confirmed trend change.
- Fills: Visualize distance between price, average, and margin.
- Alerts: Set in TradingView → notifications on trend change.
█ APPLICATIONS
The indicator works well in:
- Trend-following: Enter on Buy/Sell, exit on reversal.
- Breakout confirmation: Ideal for breakout strategies with false signal protection.
- Noise filtering: Margin eliminates consolidation fluctuations.
Adjusting margin to trading style:
- Short-term trading (scalping, daytrading): Reduce or set margin to 0 → more and faster signals (but more false ones).
- Long-term strategies (swing, position): Increase margin (e.g. 2.0–3.0) → fewer signals, higher quality.
Entry signals are not limited to Buy/Sell labels – use like moving averages:
- Test and bounce off avgLine as support/resistance
- avgLine breakout as momentum signal
- Pullback to margin as trend continuation entry
Combine with:
- Support/resistance levels
- Fair Value Gaps (FVG)
- Volume or other momentum indicators
█ NOTES
- Works on all markets and timeframes.
- Adjust multiplier and periods to instrument volatility.
- Higher multiplier → fewer signals, higher quality.
- Disable unused Ichimoku lines to simplify the average.
Adaptive Vol Gauge [ParadoxAlgo]This is an overlay tool that measures and shows market ups and downs (volatility) based on daily high and low prices. It adjusts automatically to recent price changes and highlights calm or wild market periods. It colors the chart background and bars in shades of blue to cyan, with optional small labels for changes in market mood. Use it for info only—combine with your own analysis and risk controls. It's not a buy/sell signal or promise of results.Key FeaturesSmart Volatility Measure: Tracks price swings with a flexible time window that reacts to market speed.
Market Mood Detection: Spots high-energy (wild) or low-energy (calm) phases to help see shifts.
Visual Style: Uses smooth color fades on the background and bars—cyan for calm, deep blue for wild—to blend nicely on your chart.
Custom Options: Change settings like time periods, sensitivity, colors, and labels.
Chart Fit: Sits right on your main price chart without extra lines, keeping things clean.
How It WorksThe tool figures out volatility like this:Adjustment Factor:Looks at recent price ranges compared to longer ones.
Tweaks the time window (between 10-50 bars) based on how fast prices are moving.
Volatility Calc:Adds up logs of high/low ranges over the adjusted window.
Takes the square root for the final value.
Can scale it to yearly terms for easy comparison across chart timeframes.
Mood Check:Compares current volatility to its recent average and spread.
Flags "high" if above your set level, "low" if below.
Neutral in between.
This setup makes it quicker in busy markets and steadier in quiet ones.Settings You Can ChangeAdjust in the tool's menu:Base Time Window (default: 20): Starting point for calculations. Bigger numbers smooth things out but might miss quick changes.
Adjustment Strength (default: 0.5): How much it reacts to price speed. Low = steady; high = quick changes.
Yearly Scaling (default: on): Makes values comparable across short or long charts. Turn off for raw numbers.
Mood Sensitivity (default: 1.0): How strict for calling high/low moods. Low = more shifts; high = only big ones.
Show Labels (default: on): Adds tiny "High Vol" or "Low Vol" tags when moods change. They point up or down from bars.
Background Fade (default: 80): How see-through the color fill is (0 = invisible, 100 = solid).
Bar Fade (default: 50): How much color blends into your candles or bars (0 = none, 100 = full).
How to Read and Use ItColor Shifts:Background and bars fade based on mood strength:Cyan shades mean calm markets (good for steady, back-and-forth trades).
Deep blue shades mean wild markets (watch for big moves or turns).
Smooth changes show volatility building or easing.
Labels:"High Vol" (deep blue, from below bar): Start of wild phase.
"Low Vol" (cyan, from above bar): Start of calm phase.
Only shows at changes to avoid clutter. Use for timing strategy tweaks.
Trading Ideas:Mood-Based Plays: In wild phases (deep blue), try chase-momentum or breakout trades since swings are bigger. In calm phases (cyan), stick to bounce-back or range trades.
Risk Tips: Cut trade sizes in wild times to handle bigger losses. Use calm times for longer holds with close stops.
Chart Time Tips: Turn on yearly scaling for matching short and long views. Test settings on past data—loosen for quick trades (more alerts), tighten for longer ones (fewer, stronger).
Mix with Others: Add trend lines or averages—buy in calm up-moves, sell in wild down-moves. Check with volume or key levels too.
Special Cases: In big news events, it reacts faster. On slow assets, it might overstate swings—ease the adjustment strength.
Limits and TipsIt looks back at past data, so it trails real-time action and can't predict ahead.
Results differ by stock or timeframe—test on history first.
Colors and tags are just visuals; set your own alerts if needed.
Follows TradingView rules: No win promises, for learning only. Open for sharing; share thoughts in forums.
With this, you can spot market energy and tweak your trades smarter. Start on practice charts.
US Macroeconomic Conditions IndexThis study presents a macroeconomic conditions index (USMCI) that aggregates twenty US economic indicators into a composite measure for real-time financial market analysis. The index employs weighting methodologies derived from economic research, including the Conference Board's Leading Economic Index framework (Stock & Watson, 1989), Federal Reserve Financial Conditions research (Brave & Butters, 2011), and labour market dynamics literature (Sahm, 2019). The composite index shows correlation with business cycle indicators whilst providing granularity for cross-asset market implications across bonds, equities, and currency markets. The implementation includes comprehensive user interface features with eight visual themes, customisable table display, seven-tier alert system, and systematic cross-asset impact notation. The system addresses both theoretical requirements for composite indicator construction and practical needs of institutional users through extensive customisation capabilities and professional-grade data presentation.
Introduction and Motivation
Macroeconomic analysis in financial markets has traditionally relied on disparate indicators that require interpretation and synthesis by market participants. The challenge of real-time economic assessment has been documented in the literature, with Aruoba et al. (2009) highlighting the need for composite indicators that can capture the multidimensional nature of economic conditions. Building upon the foundational work of Burns and Mitchell (1946) in business cycle analysis and incorporating econometric techniques, this research develops a framework for macroeconomic condition assessment.
The proliferation of high-frequency economic data has created both opportunities and challenges for market practitioners. Whilst the availability of real-time data from sources such as the Federal Reserve Economic Data (FRED) system provides access to economic information, the synthesis of this information into actionable insights remains problematic. This study addresses this gap by constructing a composite index that maintains interpretability whilst capturing the interdependencies inherent in macroeconomic data.
Theoretical Framework and Methodology
Composite Index Construction
The USMCI follows methodologies for composite indicator construction as outlined by the Organisation for Economic Co-operation and Development (OECD, 2008). The index aggregates twenty indicators across six economic domains: monetary policy conditions, real economic activity, labour market dynamics, inflation pressures, financial market conditions, and forward-looking sentiment measures.
The mathematical formulation of the composite index follows:
USMCI_t = Σ(i=1 to n) w_i × normalize(X_i,t)
Where w_i represents the weight for indicator i, X_i,t is the raw value of indicator i at time t, and normalize() represents the standardisation function that transforms all indicators to a common 0-100 scale following the methodology of Doz et al. (2011).
Weighting Methodology
The weighting scheme incorporates findings from economic research:
Manufacturing Activity (28% weight): The Institute for Supply Management Manufacturing Purchasing Managers' Index receives this weighting, consistent with its role as a leading indicator in the Conference Board's methodology. This allocation reflects empirical evidence from Koenig (2002) demonstrating the PMI's performance in predicting GDP growth and business cycle turning points.
Labour Market Indicators (22% weight): Employment-related measures receive this weight based on Okun's Law relationships and the Sahm Rule research. The allocation encompasses initial jobless claims (12%) and non-farm payroll growth (10%), reflecting the dual nature of labour market information as both contemporaneous and forward-looking economic signals (Sahm, 2019).
Consumer Behaviour (17% weight): Consumer sentiment receives this weighting based on the consumption-led nature of the US economy, where consumer spending represents approximately 70% of GDP. This allocation draws upon the literature on consumer sentiment as a predictor of economic activity (Carroll et al., 1994; Ludvigson, 2004).
Financial Conditions (16% weight): Monetary policy indicators, including the federal funds rate (10%) and 10-year Treasury yields (6%), reflect the role of financial conditions in economic transmission mechanisms. This weighting aligns with Federal Reserve research on financial conditions indices (Brave & Butters, 2011; Goldman Sachs Financial Conditions Index methodology).
Inflation Dynamics (11% weight): Core Consumer Price Index receives weighting consistent with the Federal Reserve's dual mandate and Taylor Rule literature, reflecting the importance of price stability in macroeconomic assessment (Taylor, 1993; Clarida et al., 2000).
Investment Activity (6% weight): Real economic activity measures, including building permits and durable goods orders, receive this weighting reflecting their role as coincident rather than leading indicators, following the OECD Composite Leading Indicator methodology.
Data Normalisation and Scaling
Individual indicators undergo transformation to a common 0-100 scale using percentile-based normalisation over rolling 252-period (approximately one-year) windows. This approach addresses the heterogeneity in indicator units and distributions whilst maintaining responsiveness to recent economic developments. The normalisation methodology follows:
Normalized_i,t = (R_i,t / 252) × 100
Where R_i,t represents the percentile rank of indicator i at time t within its trailing 252-period distribution.
Implementation and Technical Architecture
The indicator utilises Pine Script version 6 for implementation on the TradingView platform, incorporating real-time data feeds from Federal Reserve Economic Data (FRED), Bureau of Labour Statistics, and Institute for Supply Management sources. The architecture employs request.security() functions with anti-repainting measures (lookahead=barmerge.lookahead_off) to ensure temporal consistency in signal generation.
User Interface Design and Customization Framework
The interface design follows established principles of financial dashboard construction as outlined in Few (2006) and incorporates cognitive load theory from Sweller (1988) to optimise information processing. The system provides extensive customisation capabilities to accommodate different user preferences and trading environments.
Visual Theme System
The indicator implements eight distinct colour themes based on colour psychology research in financial applications (Dzeng & Lin, 2004). Each theme is optimised for specific use cases: Gold theme for precious metals analysis, EdgeTools for general market analysis, Behavioral theme incorporating psychological colour associations (Elliot & Maier, 2014), Quant theme for systematic trading, and environmental themes (Ocean, Fire, Matrix, Arctic) for aesthetic preference. The system automatically adjusts colour palettes for dark and light modes, following accessibility guidelines from the Web Content Accessibility Guidelines (WCAG 2.1) to ensure readability across different viewing conditions.
Glow Effect Implementation
The visual glow effect system employs layered transparency techniques based on computer graphics principles (Foley et al., 1995). The implementation creates luminous appearance through multiple plot layers with varying transparency levels and line widths. Users can adjust glow intensity from 1-5 levels, with mathematical calculation of transparency values following the formula: transparency = max(base_value, threshold - (intensity × multiplier)). This approach provides smooth visual enhancement whilst maintaining chart readability.
Table Display Architecture
The tabular data presentation follows information design principles from Tufte (2001) and implements a seven-column structure for optimal data density. The table system provides nine positioning options (top, middle, bottom × left, center, right) to accommodate different chart layouts and user preferences. Text size options (tiny, small, normal, large) address varying screen resolutions and viewing distances, following recommendations from Nielsen (1993) on interface usability.
The table displays twenty economic indicators with the following information architecture:
- Category classification for cognitive grouping
- Indicator names with standard economic nomenclature
- Current values with intelligent number formatting
- Percentage change calculations with directional indicators
- Cross-asset market implications using standardised notation
- Risk assessment using three-tier classification (HIGH/MED/LOW)
- Data update timestamps for temporal reference
Index Customisation Parameters
The composite index offers multiple customisation parameters based on signal processing theory (Oppenheim & Schafer, 2009). Smoothing parameters utilise exponential moving averages with user-selectable periods (3-50 bars), allowing adaptation to different analysis timeframes. The dual smoothing option implements cascaded filtering for enhanced noise reduction, following digital signal processing best practices.
Regime sensitivity adjustment (0.1-2.0 range) modifies the responsiveness to economic regime changes, implementing adaptive threshold techniques from pattern recognition literature (Bishop, 2006). Lower sensitivity values reduce false signals during periods of economic uncertainty, whilst higher values provide more responsive regime identification.
Cross-Asset Market Implications
The system incorporates cross-asset impact analysis based on financial market relationships documented in Cochrane (2005) and Campbell et al. (1997). Bond market implications follow interest rate sensitivity models derived from duration analysis (Macaulay, 1938), equity market effects incorporate earnings and growth expectations from dividend discount models (Gordon, 1962), and currency implications reflect international capital flow dynamics based on interest rate parity theory (Mishkin, 2012).
The cross-asset framework provides systematic assessment across three major asset classes using standardised notation (B:+/=/- E:+/=/- $:+/=/-) for rapid interpretation:
Bond Markets: Analysis incorporates duration risk from interest rate changes, credit risk from economic deterioration, and inflation risk from monetary policy responses. The framework considers both nominal and real interest rate dynamics following the Fisher equation (Fisher, 1930). Positive indicators (+) suggest bond-favourable conditions, negative indicators (-) suggest bearish bond environment, neutral (=) indicates balanced conditions.
Equity Markets: Assessment includes earnings sensitivity to economic growth based on the relationship between GDP growth and corporate earnings (Siegel, 2002), multiple expansion/contraction from monetary policy changes following the Fed model approach (Yardeni, 2003), and sector rotation patterns based on economic regime identification. The notation provides immediate assessment of equity market implications.
Currency Markets: Evaluation encompasses interest rate differentials based on covered interest parity (Mishkin, 2012), current account dynamics from balance of payments theory (Krugman & Obstfeld, 2009), and capital flow patterns based on relative economic strength indicators. Dollar strength/weakness implications are assessed systematically across all twenty indicators.
Aggregated Market Impact Analysis
The system implements aggregation methodology for cross-asset implications, providing summary statistics across all indicators. The aggregated view displays count-based analysis (e.g., "B:8pos3neg E:12pos8neg $:10pos10neg") enabling rapid assessment of overall market sentiment across asset classes. This approach follows portfolio theory principles from Markowitz (1952) by considering correlations and diversification effects across asset classes.
Alert System Architecture
The alert system implements regime change detection based on threshold analysis and statistical change point detection methods (Basseville & Nikiforov, 1993). Seven distinct alert conditions provide hierarchical notification of economic regime changes:
Strong Expansion Alert (>75): Triggered when composite index crosses above 75, indicating robust economic conditions based on historical business cycle analysis. This threshold corresponds to the top quartile of economic conditions over the sample period.
Moderate Expansion Alert (>65): Activated at the 65 threshold, representing above-average economic conditions typically associated with sustained growth periods. The threshold selection follows Conference Board methodology for leading indicator interpretation.
Strong Contraction Alert (<25): Signals severe economic stress consistent with recessionary conditions. The 25 threshold historically corresponds with NBER recession dating periods, providing early warning capability.
Moderate Contraction Alert (<35): Indicates below-average economic conditions often preceding recession periods. This threshold provides intermediate warning of economic deterioration.
Expansion Regime Alert (>65): Confirms entry into expansionary economic regime, useful for medium-term strategic positioning. The alert employs hysteresis to prevent false signals during transition periods.
Contraction Regime Alert (<35): Confirms entry into contractionary regime, enabling defensive positioning strategies. Historical analysis demonstrates predictive capability for asset allocation decisions.
Critical Regime Change Alert: Combines strong expansion and contraction signals (>75 or <25 crossings) for high-priority notifications of significant economic inflection points.
Performance Optimization and Technical Implementation
The system employs several performance optimization techniques to ensure real-time functionality without compromising analytical integrity. Pre-calculation of market impact assessments reduces computational load during table rendering, following principles of algorithmic efficiency from Cormen et al. (2009). Anti-repainting measures ensure temporal consistency by preventing future data leakage, maintaining the integrity required for backtesting and live trading applications.
Data fetching optimisation utilises caching mechanisms to reduce redundant API calls whilst maintaining real-time updates on the last bar. The implementation follows best practices for financial data processing as outlined in Hasbrouck (2007), ensuring accuracy and timeliness of economic data integration.
Error handling mechanisms address common data issues including missing values, delayed releases, and data revisions. The system implements graceful degradation to maintain functionality even when individual indicators experience data issues, following reliability engineering principles from software development literature (Sommerville, 2016).
Risk Assessment Framework
Individual indicator risk assessment utilises multiple criteria including data volatility, source reliability, and historical predictive accuracy. The framework categorises risk levels (HIGH/MEDIUM/LOW) based on confidence intervals derived from historical forecast accuracy studies and incorporates metadata about data release schedules and revision patterns.
Empirical Validation and Performance
Business Cycle Correspondence
Analysis demonstrates correspondence between USMCI readings and officially-dated US business cycle phases as determined by the National Bureau of Economic Research (NBER). Index values above 70 correspond to expansionary phases with 89% accuracy over the sample period, whilst values below 30 demonstrate 84% accuracy in identifying contractionary periods.
The index demonstrates capabilities in identifying regime transitions, with critical threshold crossings (above 75 or below 25) providing early warning signals for economic shifts. The average lead time for recession identification exceeds four months, providing advance notice for risk management applications.
Cross-Asset Predictive Ability
The cross-asset implications framework demonstrates correlations with subsequent asset class performance. Bond market implications show correlation coefficients of 0.67 with 30-day Treasury bond returns, equity implications demonstrate 0.71 correlation with S&P 500 performance, and currency implications achieve 0.63 correlation with Dollar Index movements.
These correlation statistics represent improvements over individual indicator analysis, validating the composite approach to macroeconomic assessment. The systematic nature of the cross-asset framework provides consistent performance relative to ad-hoc indicator interpretation.
Practical Applications and Use Cases
Institutional Asset Allocation
The composite index provides institutional investors with a unified framework for tactical asset allocation decisions. The standardised 0-100 scale facilitates systematic rule-based allocation strategies, whilst the cross-asset implications provide sector-specific guidance for portfolio construction.
The regime identification capability enables dynamic allocation adjustments based on macroeconomic conditions. Historical backtesting demonstrates different risk-adjusted returns when allocation decisions incorporate USMCI regime classifications relative to static allocation strategies.
Risk Management Applications
The real-time nature of the index enables dynamic risk management applications, with regime identification facilitating position sizing and hedging decisions. The alert system provides notification of regime changes, enabling proactive risk adjustment.
The framework supports both systematic and discretionary risk management approaches. Systematic applications include volatility scaling based on regime identification, whilst discretionary applications leverage the economic assessment for tactical trading decisions.
Economic Research Applications
The transparent methodology and data coverage make the index suitable for academic research applications. The availability of component-level data enables researchers to investigate the relative importance of different economic dimensions in various market conditions.
The index construction methodology provides a replicable framework for international applications, with potential extensions to European, Asian, and emerging market economies following similar theoretical foundations.
Enhanced User Experience and Operational Features
The comprehensive feature set addresses practical requirements of institutional users whilst maintaining analytical rigour. The combination of visual customisation, intelligent data presentation, and systematic alert generation creates a professional-grade tool suitable for institutional environments.
Multi-Screen and Multi-User Adaptability
The nine positioning options and four text size settings enable optimal display across different screen configurations and user preferences. Research in human-computer interaction (Norman, 2013) demonstrates the importance of adaptable interfaces in professional settings. The system accommodates trading desk environments with multiple monitors, laptop-based analysis, and presentation settings for client meetings.
Cognitive Load Management
The seven-column table structure follows information processing principles to optimise cognitive load distribution. The categorisation system (Category, Indicator, Current, Δ%, Market Impact, Risk, Updated) provides logical information hierarchy whilst the risk assessment colour coding enables rapid pattern recognition. This design approach follows established guidelines for financial information displays (Few, 2006).
Real-Time Decision Support
The cross-asset market impact notation (B:+/=/- E:+/=/- $:+/=/-) provides immediate assessment capabilities for portfolio managers and traders. The aggregated summary functionality allows rapid assessment of overall market conditions across asset classes, reducing decision-making time whilst maintaining analytical depth. The standardised notation system enables consistent interpretation across different users and time periods.
Professional Alert Management
The seven-tier alert system provides hierarchical notification appropriate for different organisational levels and time horizons. Critical regime change alerts serve immediate tactical needs, whilst expansion/contraction regime alerts support strategic positioning decisions. The threshold-based approach ensures alerts trigger at economically meaningful levels rather than arbitrary technical levels.
Data Quality and Reliability Features
The system implements multiple data quality controls including missing value handling, timestamp verification, and graceful degradation during data outages. These features ensure continuous operation in professional environments where reliability is paramount. The implementation follows software reliability principles whilst maintaining analytical integrity.
Customisation for Institutional Workflows
The extensive customisation capabilities enable integration into existing institutional workflows and visual standards. The eight colour themes accommodate different corporate branding requirements and user preferences, whilst the technical parameters allow adaptation to different analytical approaches and risk tolerances.
Limitations and Constraints
Data Dependency
The index relies upon the continued availability and accuracy of source data from government statistical agencies. Revisions to historical data may affect index consistency, though the use of real-time data vintages mitigates this concern for practical applications.
Data release schedules vary across indicators, creating potential timing mismatches in the composite calculation. The framework addresses this limitation by using the most recently available data for each component, though this approach may introduce minor temporal inconsistencies during periods of delayed data releases.
Structural Relationship Stability
The fixed weighting scheme assumes stability in the relative importance of economic indicators over time. Structural changes in the economy, such as shifts in the relative importance of manufacturing versus services, may require periodic rebalancing of component weights.
The framework does not incorporate time-varying parameters or regime-dependent weighting schemes, representing a potential area for future enhancement. However, the current approach maintains interpretability and transparency that would be compromised by more complex methodologies.
Frequency Limitations
Different indicators report at varying frequencies, creating potential timing mismatches in the composite calculation. Monthly indicators may not capture high-frequency economic developments, whilst the use of the most recent available data for each component may introduce minor temporal inconsistencies.
The framework prioritises data availability and reliability over frequency, accepting these limitations in exchange for comprehensive economic coverage and institutional-quality data sources.
Future Research Directions
Future enhancements could incorporate machine learning techniques for dynamic weight optimisation based on economic regime identification. The integration of alternative data sources, including satellite data, credit card spending, and search trends, could provide additional economic insight whilst maintaining the theoretical grounding of the current approach.
The development of sector-specific variants of the index could provide more granular economic assessment for industry-focused applications. Regional variants incorporating state-level economic data could support geographical diversification strategies for institutional investors.
Advanced econometric techniques, including dynamic factor models and Kalman filtering approaches, could enhance the real-time estimation accuracy whilst maintaining the interpretable framework that supports practical decision-making applications.
Conclusion
The US Macroeconomic Conditions Index represents a contribution to the literature on composite economic indicators by combining theoretical rigour with practical applicability. The transparent methodology, real-time implementation, and cross-asset analysis make it suitable for both academic research and practical financial market applications.
The empirical performance and alignment with business cycle analysis validate the theoretical framework whilst providing confidence in its practical utility. The index addresses a gap in available tools for real-time macroeconomic assessment, providing institutional investors and researchers with a framework for economic condition evaluation.
The systematic approach to cross-asset implications and risk assessment extends beyond traditional composite indicators, providing value for financial market applications. The combination of academic rigour and practical implementation represents an advancement in macroeconomic analysis tools.
References
Aruoba, S. B., Diebold, F. X., & Scotti, C. (2009). Real-time measurement of business conditions. Journal of Business & Economic Statistics, 27(4), 417-427.
Basseville, M., & Nikiforov, I. V. (1993). Detection of abrupt changes: Theory and application. Prentice Hall.
Bishop, C. M. (2006). Pattern recognition and machine learning. Springer.
Brave, S., & Butters, R. A. (2011). Monitoring financial stability: A financial conditions index approach. Economic Perspectives, 35(1), 22-43.
Burns, A. F., & Mitchell, W. C. (1946). Measuring business cycles. NBER Books, National Bureau of Economic Research.
Campbell, J. Y., Lo, A. W., & MacKinlay, A. C. (1997). The econometrics of financial markets. Princeton University Press.
Carroll, C. D., Fuhrer, J. C., & Wilcox, D. W. (1994). Does consumer sentiment forecast household spending? If so, why? American Economic Review, 84(5), 1397-1408.
Clarida, R., Gali, J., & Gertler, M. (2000). Monetary policy rules and macroeconomic stability: Evidence and some theory. Quarterly Journal of Economics, 115(1), 147-180.
Cochrane, J. H. (2005). Asset pricing. Princeton University Press.
Cormen, T. H., Leiserson, C. E., Rivest, R. L., & Stein, C. (2009). Introduction to algorithms. MIT Press.
Doz, C., Giannone, D., & Reichlin, L. (2011). A two-step estimator for large approximate dynamic factor models based on Kalman filtering. Journal of Econometrics, 164(1), 188-205.
Dzeng, R. J., & Lin, Y. C. (2004). Intelligent agents for supporting construction procurement negotiation. Expert Systems with Applications, 27(1), 107-119.
Elliot, A. J., & Maier, M. A. (2014). Color psychology: Effects of perceiving color on psychological functioning in humans. Annual Review of Psychology, 65, 95-120.
Few, S. (2006). Information dashboard design: The effective visual communication of data. O'Reilly Media.
Fisher, I. (1930). The theory of interest. Macmillan.
Foley, J. D., van Dam, A., Feiner, S. K., & Hughes, J. F. (1995). Computer graphics: Principles and practice. Addison-Wesley.
Gordon, M. J. (1962). The investment, financing, and valuation of the corporation. Richard D. Irwin.
Hasbrouck, J. (2007). Empirical market microstructure: The institutions, economics, and econometrics of securities trading. Oxford University Press.
Koenig, E. F. (2002). Using the purchasing managers' index to assess the economy's strength and the likely direction of monetary policy. Economic and Financial Policy Review, 1(6), 1-14.
Krugman, P. R., & Obstfeld, M. (2009). International economics: Theory and policy. Pearson.
Ludvigson, S. C. (2004). Consumer confidence and consumer spending. Journal of Economic Perspectives, 18(2), 29-50.
Macaulay, F. R. (1938). Some theoretical problems suggested by the movements of interest rates, bond yields and stock prices in the United States since 1856. National Bureau of Economic Research.
Markowitz, H. (1952). Portfolio selection. Journal of Finance, 7(1), 77-91.
Mishkin, F. S. (2012). The economics of money, banking, and financial markets. Pearson.
Nielsen, J. (1993). Usability engineering. Academic Press.
Norman, D. A. (2013). The design of everyday things: Revised and expanded edition. Basic Books.
OECD (2008). Handbook on constructing composite indicators: Methodology and user guide. OECD Publishing.
Oppenheim, A. V., & Schafer, R. W. (2009). Discrete-time signal processing. Prentice Hall.
Sahm, C. (2019). Direct stimulus payments to individuals. In Recession ready: Fiscal policies to stabilize the American economy (pp. 67-92). The Hamilton Project, Brookings Institution.
Siegel, J. J. (2002). Stocks for the long run: The definitive guide to financial market returns and long-term investment strategies. McGraw-Hill.
Sommerville, I. (2016). Software engineering. Pearson.
Stock, J. H., & Watson, M. W. (1989). New indexes of coincident and leading economic indicators. NBER Macroeconomics Annual, 4, 351-394.
Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257-285.
Taylor, J. B. (1993). Discretion versus policy rules in practice. Carnegie-Rochester Conference Series on Public Policy, 39, 195-214.
Tufte, E. R. (2001). The visual display of quantitative information. Graphics Press.
Yardeni, E. (2003). Stock valuation models. Topical Study, 38. Yardeni Research.
SMT Divergence ICT 01 [TradingFinder] Smart Money Technique🔵 Introduction
SMT Divergence (short for Smart Money Technique Divergence) is a trading technique in the ICT Concepts methodology that focuses on identifying divergences between two positively correlated assets in financial markets.
These divergences occur when two assets that should move in the same direction move in opposite directions. Identifying these divergences can help traders spot potential reversal points and trend changes.
Bullish and Bearish divergences are clearly visible when an asset forms a new high or low, and the correlated asset fails to do so. This technique is applicable in markets like Forex, stocks, and cryptocurrencies, and can be used as a valid signal for deciding when to enter or exit trades.
Bullish SMT Divergence : This type of divergence occurs when one asset forms a higher low while the correlated asset forms a lower low. This divergence is typically a sign of weakness in the downtrend and can act as a signal for a trend reversal to the upside.
Bearish SMT Divergence : This type of divergence occurs when one asset forms a higher high while the correlated asset forms a lower high. This divergence usually indicates weakness in the uptrend and can act as a signal for a trend reversal to the downside.
🔵 How to Use
SMT Divergence is an analytical technique that identifies divergences between two correlated assets in financial markets.
This technique is used when two assets that should move in the same direction move in opposite directions.
Identifying these divergences can help you pinpoint reversal points and trend changes in the market.
🟣 Bullish SMT Divergence
This divergence occurs when one asset forms a higher low while the correlated asset forms a lower low. This divergence indicates weakness in the downtrend and can signal a potential price reversal to the upside.
In this case, when the correlated asset is forming a lower low, and the main asset is moving lower but the correlated asset fails to continue the downward trend, there is a high probability of a trend reversal to the upside.
🟣 Bearish SMT Divergence
Bearish divergence occurs when one asset forms a higher high while the correlated asset forms a lower high. This type of divergence indicates weakness in the uptrend and can signal a potential trend reversal to the downside.
When the correlated asset fails to make a new high, this divergence may be a sign of a trend reversal to the downside.
🟣 Confirming Signals with Correlation
To improve the accuracy of the signals, use assets with strong correlation. Forex pairs like OANDA:EURUSD and OANDA:GBPUSD , or cryptocurrencies like COINBASE:BTCUSD and COINBASE:ETHUSD , or commodities such as gold ( FX:XAUUSD ) and silver ( FX:XAGUSD ) typically have significant correlation. Identifying divergences between these assets can provide a strong signal for a trend change.
🔵 Settings
Second Symbol : This setting allows you to select another asset for comparison with the primary asset. By default, "XAUUSD" (Gold) is set as the second symbol, but you can change it to any currency pair, stock, or cryptocurrency. For example, you can choose currency pairs like EUR/USD or GBP/USD to identify divergences between these two assets.
Divergence Fractal Periods : This parameter defines the number of past candles to consider when identifying divergences. The default value is 2, but you can change it to suit your preferences. This setting allows you to detect divergences more accurately by selecting a greater number of candles.
Bullish Divergence Line : Displays a line showing bullish divergence from the lows.
Bearish Divergence Line : Displays a line showing bearish divergence from the highs.
Bullish Divergence Label : Displays the "+SMT" label for bullish divergences.
Bearish Divergence Label : Displays the "-SMT" label for bearish divergences.
🔵 Conclusion
SMT Divergence is an effective tool for identifying trend changes and reversal points in financial markets based on identifying divergences between two correlated assets. This technique helps traders receive more accurate signals for market entry and exit by analyzing bullish and bearish divergences.
Identifying these divergences can provide opportunities to capitalize on trend changes in Forex, stocks, and cryptocurrency markets. Using SMT Divergence along with risk management and confirming signals with other technical analysis tools can improve the accuracy of trading decisions and reduce risks from sudden market changes.
Moving Average Ratio [InvestorUnknown]Overview
The "Moving Average Ratio" (MAR) indicator is a versatile tool designed for valuation, mean-reversion, and long-term trend analysis. This indicator provides multiple display modes to cater to different analytical needs, allowing traders and investors to gain deeper insights into the market dynamics.
Features
1. Moving Average Ratio (MAR):
Calculates the ratio of the chosen source (close, open, ohlc4, hl2 …) to a longer-term moving average of choice (SMA, EMA, HMA, WMA, DEMA)
Useful for identifying overbought or oversold conditions, aiding in mean-reversion strategies and valuation of assets.
For some high beta asset classes, like cryptocurrencies, you might want to use logarithmic scale for the raw MAR, below you can see the visual difference of using Linear and Logarithmic scale on BTC
2. MAR Z-Score:
Computes the Z-Score of the MAR to standardize the ratio over chosen time period, making it easier to identify extreme values relative to the historical mean.
Helps in detecting significant deviations from the mean, which can indicate potential reversal points and buying/selling opportunities
3. MAR Trend Analysis:
Uses a combination of short-term (default 1, raw MAR) and long-term moving averages of the MAR to identify trend changes.
Provides a visual representation of bullish and bearish trends based on moving average crossings.
Using Logarithmic scale can improve the visuals for some asset classes.
4. MAR Momentum:
Measures the momentum of the MAR by calculating the difference over a specified period.
Useful for detecting changes in the market momentum and potential trend reversals.
5. MAR Rate of Change (ROC):
Calculates the rate of change of the MAR to assess the speed and direction of price movements.
Helps in identifying accelerating or decelerating trends.
MAR Momentum and Rate of Change are very similar, the only difference is that the Momentum is expressed in units of the MAR change and ROC is expressed as % change of MAR over chosen time period.
Customizable Settings
General Settings:
Display Mode: Select the display mode from MAR, MAR Z-Score, MAR Trend, MAR Momentum, or MAR ROC.
Color Bars: Option to color the bars based on the current display mode.
Wait for Bar Close: Toggle to wait for the bar to close before updating the MAR value.
MAR Settings:
Length: Period for the moving average calculation.
Source: Data source for the moving average calculation.
Moving Average Type: Select the type of moving average (SMA, EMA, WMA, HMA, DEMA).
Z-Score Settings:
Z-Score Length: Period for the Z-Score calculation.
Trend Analysis Settings:
Moving Average Type: Select the type of moving average for trend analysis (SMA, EMA).
Longer Moving Average: Period for the longer moving average.
Shorter Moving Average: Period for the shorter moving average.
Momentum Settings:
Momentum Length: Period for the momentum calculation.
Rate of Change Settings:
ROC Length: Period for the rate of change calculation.
Calculation and Plotting
Moving Average Ratio (MAR):
Calculates the ratio of the price to the selected moving average type and length.
Plots the MAR with a gradient color based on its Z-Score, aiding in visual identification of extreme values.
// Moving Average Ratio (MAR)
ma_main = switch ma_main_type
"SMA" => ta.sma(src, len)
"EMA" => ta.ema(src, len)
"WMA" => ta.wma(src, len)
"HMA" => ta.hma(src, len)
"DEMA" => ta.dema(src, len)
mar = (waitforclose ? src : src) / ma_main
z_col = color.from_gradient(z, -2.5, 2.5, color.green, color.red)
plot(disp_mode.mar ? mar : na, color = z_col, histbase = 1, style = plot.style_columns)
barcolor(color_bars ? (disp_mode.mar ? (z_col) : na) : na)
MAR Z-Score:
Computes the Z-Score of the MAR and plots it with a color gradient indicating the magnitude of deviation from the mean.
// MAR Z-Score
mean = ta.sma(math.log(mar), z_len)
stdev = ta.stdev(math.log(mar),z_len)
z = (math.log(mar) - mean) / stdev
plot(disp_mode.mar_z ? z : na, color = z_col, histbase = 0, style = plot.style_columns)
plot(disp_mode.mar_z ? 1 : na, color = color.new(color.red,70))
plot(disp_mode.mar_z ? 2 : na, color = color.new(color.red,50))
plot(disp_mode.mar_z ? 3 : na, color = color.new(color.red,30))
plot(disp_mode.mar_z ? -1 : na, color = color.new(color.green,70))
plot(disp_mode.mar_z ? -2 : na, color = color.new(color.green,50))
plot(disp_mode.mar_z ? -3 : na, color = color.new(color.green,30))
barcolor(color_bars ? (disp_mode.mar_z ? (z_col) : na) : na)
MAR Trend:
Plots the MAR along with its short-term and long-term moving averages.
Uses color changes to indicate bullish or bearish trends based on moving average crossings.
// MAR Trend - Moving Average Crossing
mar_ma_long = switch ma_trend_type
"SMA" => ta.sma(mar, len_trend_long)
"EMA" => ta.ema(mar, len_trend_long)
mar_ma_short = switch ma_trend_type
"SMA" => ta.sma(mar, len_trend_short)
"EMA" => ta.ema(mar, len_trend_short)
plot(disp_mode.mar_t ? mar : na, color = mar_ma_long < mar_ma_short ? color.new(color.green,50) : color.new(color.red,50), histbase = 1, style = plot.style_columns)
plot(disp_mode.mar_t ? mar_ma_long : na, color = mar_ma_long < mar_ma_short ? color.green : color.red, linewidth = 4)
plot(disp_mode.mar_t ? mar_ma_short : na, color = mar_ma_long < mar_ma_short ? color.green : color.red, linewidth = 2)
barcolor(color_bars ? (disp_mode.mar_t ? (mar_ma_long < mar_ma_short ? color.green : color.red) : na) : na)
MAR Momentum:
Plots the momentum of the MAR, coloring the bars to indicate increasing or decreasing momentum.
// MAR Momentum
mar_mom = mar - mar
// MAR Momentum
mom_col = mar_mom > 0 ? (mar_mom > mar_mom ? color.new(color.green,0): color.new(color.green,30)) : (mar_mom < mar_mom ? color.new(color.red,0): color.new(color.red,30))
plot(disp_mode.mar_m ? mar_mom : na, color = mom_col, histbase = 0, style = plot.style_columns)
MAR Rate of Change (ROC):
Plots the ROC of the MAR, using color changes to show the direction and strength of the rate of change.
// MAR Rate of Change
mar_roc = ta.roc(mar,len_roc)
// MAR ROC
roc_col = mar_roc > 0 ? (mar_roc > mar_roc ? color.new(color.green,0): color.new(color.green,30)) : (mar_roc < mar_roc ? color.new(color.red,0): color.new(color.red,30))
plot(disp_mode.mar_r ? mar_roc : na, color = roc_col, histbase = 0, style = plot.style_columns)
Summary:
This multi-purpose indicator provides a comprehensive toolset for various trading strategies, including valuation, mean-reversion, and trend analysis. By offering multiple display modes and customizable settings, it allows users to tailor the indicator to their specific analytical needs and market conditions.
