Triple-Filter ConfirmationTriple-Filter Confirmation System
This indicator generates high-probability trading signals based on a 3-layer filtering approach:
🔹 Trend Filter – Uses a 200-period EMA slope to confirm bullish or bearish bias.
🔹 Momentum Filter – Uses MACD histogram direction for secondary confirmation.
🔹 Volatility Filter – Filters out weak setups using ATR percentile rank (relative to last 100 bars).
✅ Signal appears only when all filters align, avoiding noise and low-confidence zones.
🚫 If any filter disagrees, no signal is shown — preserving capital through discipline.
💡 Works across any timeframe and asset. Use it alongside price action, support/resistance, and sound risk management.
Created for educational and research purposes — not financial advice.
Momentum Indicator (MOM)
MACD Breakout SuperCandlesMACD Breakout SuperCandles
The MACD Breakout SuperCandles indicator is a candle-coloring tool that monitors trend alignment across multiple timeframes using a combination of MACD behavior and simple price structure. It visually reflects market sentiment directly on price candles, helping traders quickly recognize shifting momentum conditions.
How It Works
The script evaluates trend behavior based on:
- Multi-timeframe MACD Analysis: Uses MACD values and signal line relationships to gauge trend direction and strength.
- Price Relative to SMA Zones: Analyzes whether price is positioned above or below the 20-period high and low SMAs on each timeframe.
For each timeframe, the script assigns one of five possible trend statuses:
- SUPERBULL: Strong bullish MACD signal with price above both SMAs.
- Bullish: Bullish MACD crossover with price showing upward bias.
- Basing: MACD flattening or neutralizing near zero with no directional dominance.
- Bearish: Bearish MACD signal without confirmation of stronger trend.
- SUPERBEAR: Strong bearish MACD signal with price below both SMAs.
-Ghost Candles: Candles with basing attributes that can signal directional change or trend strength.
Signal Scoring System
The script compares conditions across four timeframes:
- TF1 (Short)
- TF2 (Medium)
- TF3 (Long)
- MACD at a fixed 10-minute resolution
Each status type is tracked independently. A colored candle is only applied when a status type (e.g., SUPERBULL) reaches the minimum match threshold, defined by the "Min Status Matches for Candle Color" setting. If no status meets the required threshold, the candle is displayed in a neutral "Ghost" color.
Customizable Visuals
The indicator offers full control over candle appearance via grouped settings:
Body Colors
- SUPERBULL Body
- Bullish Body
- Basing Body
- Bearish Body
- SUPERBEAR Body
- Ghost Candle Body (used when no match)
Border & Wick Colors
- SUPERBULL Border/Wick
- Bullish Border/Wick
- Basing Border/Wick
- Bearish Border/Wick
- SUPERBEAR Border/Wick
- Ghost Border/Wick
Colors are grouped by function and can be adjusted independently to match your chart theme or personal preferences.
Settings Overview
- TF1, TF2, TF3: Select short, medium, and long timeframes to monitor trend structure.
- Min Status Matches: Set how many timeframes must agree before a candle status is applied.
- MACD Settings: Customize MACD fast, slow, and signal lengths, and choose MA type (EMA, SMA, WMA).
This tool helps visualize how aligned various timeframe conditions are by embedding sentiment into the candles themselves. It can assist with trend identification, momentum confirmation, or visual filtering for discretionary strategies.
Magnificent 7 OscillatorThe Magnificent 7 Oscillator is a sophisticated momentum-based technical indicator designed to analyze the collective performance of the seven largest technology companies in the U.S. stock market (Apple, Microsoft, Alphabet, Amazon, NVIDIA, Tesla, and Meta). This indicator incorporates established momentum factor research and provides three distinct analytical modes: absolute momentum tracking, equal-weighted market comparison, and relative performance analysis. The tool integrates five different oscillator methodologies and includes advanced breadth analysis capabilities.
Theoretical Foundation
Momentum Factor Research
The indicator's foundation rests on seminal momentum research in financial markets. Jegadeesh and Titman (1993) demonstrated that stocks with strong price performance over 3-12 month periods tend to continue outperforming in subsequent periods¹. This momentum effect was later incorporated into formal factor models by Carhart (1997), who extended the Fama-French three-factor model to include a momentum factor (UMD - Up Minus Down)².
The momentum calculation methodology follows the academic standard:
Momentum(t) = / P(t-n) × 100
Where P(t) is the current price and n is the lookback period.
The focus on the "Magnificent 7" stocks reflects the increasing market concentration observed in recent years. Fama and French (2015) noted that a small number of large-cap stocks can drive significant market movements due to their substantial index weights³. The combined market capitalization of these seven companies often exceeds 25% of the total S&P 500, making their collective momentum a critical market indicator.
Indicator Architecture
Core Components
1. Data Collection and Processing
The indicator employs robust data collection with error handling for missing or invalid security data. Each stock's momentum is calculated independently using the specified lookback period (default: 14 periods).
2. Composite Oscillator Calculation
Following Fama-French factor construction methodology, the indicator offers two weighting schemes:
- Equal Weight: Each active stock receives identical weighting (1/n)
- Market Cap Weight: Reserved for future enhancement
3. Oscillator Transformation Functions
The indicator provides five distinct oscillator types, each with established technical analysis foundations:
a) Momentum Oscillator (Default)
- Pure rate-of-change calculation
- Centered around zero
- Direct implementation of Jegadeesh & Titman methodology
b) RSI (Relative Strength Index)
- Wilder's (1978) relative strength methodology
- Transformed to center around zero for consistency
- Scale: -50 to +50
c) Stochastic Oscillator
- George Lane's %K methodology
- Measures current position within recent range
- Transformed to center around zero
d) Williams %R
- Larry Williams' range-based oscillator
- Inverse stochastic calculation
- Adjusted for zero-centered display
e) CCI (Commodity Channel Index)
- Donald Lambert's mean reversion indicator
- Measures deviation from moving average
- Scaled for optimal visualization
Operational Modes
Mode 1: Magnificent 7 Analysis
Tracks the collective momentum of the seven constituent stocks. This mode is optimal for:
- Technology sector analysis
- Growth stock momentum assessment
- Large-cap performance tracking
Mode 2: S&P 500 Equal Weight Comparison
Analyzes momentum using an equal-weighted S&P 500 reference (typically RSP ETF). This mode provides:
- Broader market momentum context
- Size-neutral market analysis
- Comparison baseline for relative performance
Mode 3: Relative Performance Analysis
Calculates the momentum differential between Magnificent 7 and S&P 500 Equal Weight. This mode enables:
- Sector rotation analysis
- Style factor assessment (Growth vs. Value)
- Relative strength identification
Formula: Relative Performance = MAG7_Momentum - SP500EW_Momentum
Signal Generation and Thresholds
Signal Classification
The indicator generates three signal states:
- Bullish: Oscillator > Upper Threshold (default: +2.0%)
- Bearish: Oscillator < Lower Threshold (default: -2.0%)
- Neutral: Oscillator between thresholds
Relative Performance Signals
In relative performance mode, specialized thresholds apply:
- Outperformance: Relative momentum > +1.0%
- Underperformance: Relative momentum < -1.0%
Alert System
Comprehensive alert conditions include:
- Threshold crossovers (bullish/bearish signals)
- Zero-line crosses (momentum direction changes)
- Relative performance shifts
- Breadth Analysis Component
The indicator incorporates market breadth analysis, calculating the percentage of constituent stocks with positive momentum. This feature provides insights into:
- Strong Breadth (>60%): Broad-based momentum
- Weak Breadth (<40%): Narrow momentum leadership
- Mixed Breadth (40-60%): Neutral momentum distribution
Visual Design and User Interface
Theme-Adaptive Display
The indicator automatically adjusts color schemes for dark and light chart themes, ensuring optimal visibility across different user preferences.
Professional Data Table
A comprehensive data table displays:
- Current oscillator value and percentage
- Active mode and oscillator type
- Signal status and strength
- Component breakdowns (in relative performance mode)
- Breadth percentage
- Active threshold levels
Custom Color Options
Users can override default colors with custom selections for:
- Neutral conditions (default: Material Blue)
- Bullish signals (default: Material Green)
- Bearish signals (default: Material Red)
Practical Applications
Portfolio Management
- Sector Allocation: Use relative performance mode to time technology sector exposure
- Risk Management: Monitor breadth deterioration as early warning signal
- Entry/Exit Timing: Utilize threshold crossovers for position sizing decisions
Market Analysis
- Trend Identification: Zero-line crosses indicate momentum regime changes
- Divergence Analysis: Compare MAG7 performance against broader market
- Volatility Assessment: Oscillator range and frequency provide volatility insights
Strategy Development
- Factor Timing: Implement growth factor timing strategies
- Momentum Strategies: Develop systematic momentum-based approaches
- Risk Parity: Use breadth metrics for risk-adjusted portfolio construction
Configuration Guidelines
Parameter Selection
- Momentum Period (5-100): Shorter periods (5-20) for tactical analysis, longer periods (50-100) for strategic assessment
- Smoothing Period (1-50): Higher values reduce noise but increase lag
- Thresholds: Adjust based on historical volatility and strategy requirements
Timeframe Considerations
- Daily Charts: Optimal for swing trading and medium-term analysis
- Weekly Charts: Suitable for long-term trend analysis
- Intraday Charts: Useful for short-term tactical decisions
Limitations and Considerations
Market Concentration Risk
The indicator's focus on seven stocks creates concentration risk. During periods of significant rotation away from large-cap technology stocks, the indicator may not represent broader market conditions.
Momentum Persistence
While momentum effects are well-documented, they are not permanent. Jegadeesh and Titman (1993) noted momentum reversal effects over longer time horizons (2-5 years).
Correlation Dynamics
During market stress, correlations among the constituent stocks may increase, reducing the diversification benefits and potentially amplifying signal intensity.
Performance Metrics and Backtesting
The indicator includes hidden plots for comprehensive backtesting:
- Individual stock momentum values
- Composite breadth percentage
- S&P 500 Equal Weight momentum
- Relative performance calculations
These metrics enable quantitative strategy development and historical performance analysis.
References
¹Jegadeesh, N., & Titman, S. (1993). Returns to buying winners and selling losers: Implications for stock market efficiency. Journal of Finance, 48(1), 65-91.
Carhart, M. M. (1997). On persistence in mutual fund performance. Journal of Finance, 52(1), 57-82.
Fama, E. F., & French, K. R. (2015). A five-factor asset pricing model. Journal of Financial Economics, 116(1), 1-22.
Wilder, J. W. (1978). New concepts in technical trading systems. Trend Research.
CDP - Counter-Directional-Pivot🎯 CDP - Counter-Directional-Pivot
📊 Overview
The Counter-Directional-Pivot (CDP) indicator calculates five critical price levels based on the previous day's OHLC data, specifically designed for multi-timeframe analysis. Unlike standard pivot points, CDP levels are calculated using a unique formula that identifies potential reversal zones where price action often changes direction.
⚡ What Makes This Script Original
This implementation solves several technical challenges that existing pivot indicators face:
🔄 Multi-Timeframe Consistency: Values remain identical across all timeframes (1m, 5m, 1h, daily) - a common problem with many pivot implementations
🔒 Intraday Stability: Uses advanced value-locking technology to prevent the "stepping" effect that occurs when pivot lines shift during the trading session
💪 Robust Data Handling: Optimized for both liquid and illiquid stocks with enhanced data synchronization
🧮 CDP Calculation Formula
The indicator calculates five key levels using the previous day's High (H), Low (L), and Close (C):
CDP = (H + L + C) ÷ 3 (Central Decision Point)
AH = 2×CDP + H – 2×L (Anchor High - Strong Resistance)
NH = 2×CDP – L (Near High - Moderate Resistance)
AL = 2×CDP – 2×H + L (Anchor Low - Strong Support)
NL = 2×CDP – H (Near Low - Moderate Support)
✨ Key Features
🎨 Visual Elements
📈 Five Distinct Price Levels: Each with customizable colors and line styles
🏷️ Smart Label System: Shows exact price values for each level
📋 Optional Value Table: Displays all levels in an organized table format
🎯 Clean Chart Display: Minimal visual clutter while maximizing information
⚙️ Technical Advantages
🔐 Session-Locked Values: Prices are locked at market open, preventing intraday shifts
🔄 Multi-Timeframe Sync: Perfect consistency between daily and intraday charts
✅ Data Validation: Built-in checks ensure reliable calculations
🚀 Performance Optimized: Efficient code structure for fast loading
💼 Trading Applications
🔄 Reversal Zones: AH and AL often act as strong turning points
💥 Breakout Confirmation: Price movement beyond these levels signals trend continuation
🛡️ Risk Management: Use levels for stop-loss and take-profit placement
🏗️ Market Structure: Understand daily ranges and potential price targets
📚 How to Use
🚀 Basic Setup
Add the indicator to your chart (works on any timeframe)
Customize colors for easy identification of support/resistance zones
Enable the value table for quick reference of exact price levels
📈 Trading Strategy Examples
🟢 Long Bias: Look for bounces at NL or AL levels
🔴 Short Bias: Watch for rejections at NH or AH levels
💥 Breakout Trading: Enter positions when price decisively breaks through anchor levels
↔️ Range Trading: Use CDP as the central reference point for range-bound markets
🎯 Advanced Strategy Combinations
RSI Integration for Enhanced Signals: 📊
📉 Oversold Bounces: Combine RSI below 30 with price touching AL/NL levels for high-probability long entries
📈 Overbought Rejections: Look for RSI above 70 with price rejecting AH/NH levels for short opportunities
🔍 Divergence Confirmation: When RSI shows bullish divergence at support levels (AL/NL) or bearish divergence at resistance levels (AH/NH), it often signals stronger reversal potential
⚡ Momentum Confluence: RSI crossing 50 while price breaks through CDP can confirm trend direction changes
⚙️ Configuration Options
🎨 Line Customization: Adjust width, style (solid/dashed/dotted), and colors
👁️ Display Preferences: Toggle individual levels, labels, and value table
📍 Table Position: Place the value table anywhere on your chart
🔔 Alert System: Get notifications when price crosses key levels
🔧 Technical Implementation Details
🎯 Data Reliability
The script uses request.security() with lookahead settings to ensure historical accuracy while maintaining real-time functionality. The value-locking mechanism prevents the common issue where pivot levels shift during the trading day.
🔄 Multi-Timeframe Logic
⏰ Intraday Charts: Display previous day's calculated levels as stable horizontal lines
📅 Daily Charts: Show current day's levels based on yesterday's OHLC
🔍 Consistency Check: All timeframes reference the same source data
🤔 Why CDP vs Standard Pivots?
Counter-Directional Pivots often provide more accurate reversal points than traditional pivot calculations because they incorporate the relationship between high/low ranges and closing prices more effectively. The formula creates levels that better reflect market psychology and institutional trading behaviors.
💡 Best Practices
💧 Use on liquid markets for most reliable results
📊 RSI Combination: Add RSI indicator for overbought/oversold confirmation and divergence analysis
📊 Combine with volume analysis for confirmation
🔍 Consider multiple timeframe analysis (daily levels on hourly charts)
📝 Test thoroughly in paper trading before live implementation
💪 Example Market Applications
NASDAQ:AAPL AAPL - Tech stock breakouts through AH levels
$NYSE:SPY SPY - Index trading with CDP range analysis
NASDAQ:TSLA TSLA - Volatile stock reversals at AL/NL levels
⚠️ This indicator is designed for educational and analytical purposes. Always combine with proper risk management and additional technical analysis tools.
MACD Histogram on RSI - Hex ColorsThe Moving Average Convergence Divergence ( MACD ) indicator was developed by Gerald Appel in the late 1970s as a tool for identifying changes in momentum, trend strength, and direction in financial markets . Appel designed MACD to provide traders with a clearer view of market trends by comparing two exponential moving averages (EMAs) and their convergence or divergence over time. The indicator became widely popular due to its versatility—it helps traders recognize strong trends while also signaling potential reversals. Over the years, MACD has evolved, with refinements in interpretation and parameter settings, making it a staple in technical analysis. The most impotrtant indications given by MACD are divergences .
MACD divergences are classified into different types based on their strength and reliability in predicting trend reversals . Here are the main classes:
Class A Divergence: This is the strongest type of divergence. It occurs when the price makes a new high (or low), but the MACD fails to confirm it by making a lower high (or higher low). This signals a high probability of trend reversal.
Class B Divergence: In this case, the price forms a double top or double bottom, but the MACD does not reach a new extreme. This suggests a potential reversal but with less certainty compared to Class A.
Class C Divergence: The weakest form of divergence, where the price makes a new high or low, but the MACD forms a pattern similar to a double top or double bottom. This indicates a possible slowdown in momentum rather than a strong reversal.
These divergences help traders assess whether a trend is losing strength and may reverse.
Besides these, there are two other signals that traders should be aware of, viz, ZLR and Shamur signal.
The Zero Line Reject (ZLR) is a concept in MACD analysis where the MACD line approaches the zero line, briefly crosses it, and then reverses direction. This behavior suggests that the trend attempted to shift but failed, reinforcing the prevailing trend. Traders often interpret this as a continuation signal rather than a reversal.
The Shamur Signal , as some traders call it, is a variation of this pattern. It occurs when the MACD line drops below the zero line, rebounds above it, and then resumes its downward movement—or vice versa for bullish setups. This pattern can indicate a false breakout or a temporary shift in momentum before the trend resumes. The key takeaway is that the market attempted to reverse but lacked the strength to sustain the move, making it a potential opportunity for trend traders.
Now let's look at RSI(14) briefly: The Relative Strength Index (RSI) is a widely used momentum oscillator that measures the speed and magnitude of price movements to identify overbought and oversold conditions. Developed by J. Welles Wilder Jr. in 1978, RSI operates on a scale from 0 to 100, with readings above 70 typically indicating an overbought market and readings below 30 signaling an oversold market. Traders use RSI to assess trend strength, spot potential reversals, and confirm price movements. While effective in ranging markets, RSI can also be adapted with divergence analysis and dynamic thresholds to enhance its predictive power.
Now, the question arises why do we use an indicator on indicator?
Using indicator-on-indicator analysis enhances traditional technical indicators by applying secondary calculations to their values, unlocking deeper insights into market behavior. This method offers several advantages:
Refined Signal Filtering – Applying an indicator to another indicator smooths out noise, helping traders avoid false signals and focus on meaningful market trends. For example, using MACD on RSI can reveal momentum shifts that standard RSI alone might overlook.
Multi-Layered Confirmation – Instead of relying on a single indicator, traders get a more nuanced view of price movements. Secondary indicators reinforce decisions, improving accuracy in identifying trend strength and reversals.
Adaptive Market Analysis – Different market environments require different tools. Indicator-on-indicator techniques allow traders to fine-tune strategies based on changing volatility and momentum conditions rather than relying on static thresholds.
Creative Customization – Traders can mold indicators to fit their specific market approach. Whether refining entries/exits or detecting trend exhaustion, these hybrid setups provide tailored insights beyond conventional methods.
This approach is particularly useful for momentum and trend-based trading, offering a more dynamic perspective that adapts to price action in a way traditional indicators cannot.
What are the potential shortcomings of such an approach?
While indicator-on-indicator analysis can refine signals and enhance decision-making, it also comes with several drawbacks that traders should consider:
Lagging Effect – Since indicators are already derivatives of price action, stacking them introduces additional delays in responsiveness. This can lead to late entries or exits, reducing a strategy’s effectiveness in fast-moving markets.
Over-Filtering Signals – Applying an indicator to another can smooth out noise, but it may also suppress valuable early signals. Traders may miss key turning points if too much filtering dilutes the raw market momentum.
Complex Interpretation – Standard indicators have well-defined thresholds and behaviors, but once modified by another indicator, they can become harder to interpret. Traders may struggle to adapt existing strategies or find reliable patterns.
Reduced Versatility – Some hybrid indicators work well in specific market conditions but lose their edge in others. This dependency on particular trends or volatility levels can make a strategy less adaptable.
Potential Redundancy – If indicators are not chosen wisely, layering them may lead to excessive confirmation bias, where multiple indicators show similar information without providing any new insights.
While indicator-on-indicator techniques can refine analysis, careful calibration is required to balance precision with practicality.
The MACD on RSI Indicator merges two powerful momentum-based indicators, offering deeper insights into trend dynamics and market strength . By applying the MACD calculation to the RSI values instead of price, traders can detect subtle shifts in momentum that might be overlooked by traditional MACD or RSI alone.
This hybrid approach enhances trend confirmation , allowing traders to gauge whether RSI’s momentum aligns with MACD's trend direction. It helps in early signal detection , potentially revealing trend shifts before they appear on conventional setups. Additionally, it reduces false signals by filtering RSI fluctuations, making MACD more reactive to meaningful changes in strength rather than short-term noise.
By combining these indicators, traders can refine entries and exits based on momentum divergences, zero-line behaviors, and shifts in trend acceleration. The MACD on RSI setup is particularly useful in identifying trend exhaustion and continuation signals, making it a valuable tool in both ranging and trending markets.
I have primarily used this indicator to spot hidden divergences. So what are they?
Hidden divergences , sometimes referred to as reverse divergences , are a powerful yet often overlooked concept in technical analysis. Unlike regular divergences, which signal potential trend reversals, hidden divergences indicate trend continuation —suggesting that the prevailing trend is likely to persist despite temporary price fluctuations.
Hidden divergences occur when the price makes a higher low in an uptrend or a lower high in a downtrend, while the oscillator (such as RSI, MACD, or Stochastic) forms a lower low or higher high, respectively. This discrepancy suggests that momentum is still strong in the direction of the trend, even though price action may appear to weaken momentarily.
Types of Hidden Divergences
---------------------------------------------------------
Hidden Bullish Divergence: Price forms a higher low, but the oscillator prints a lower low. This signals that the uptrend remains intact and is likely to continue.
Hidden Bearish Divergence: Price forms a lower high, but the oscillator prints a higher high. This suggests that the downtrend is still dominant and likely to persist.
Why Hidden Divergences Matter
Hidden divergences are particularly useful for trend-following traders, as they provide early confirmation that a trend is still strong despite minor pullbacks. They help traders avoid premature exits and reinforce confidence in holding positions longer. Additionally, hidden divergences can serve as entry signals, allowing traders to position themselves in the direction of the trend before a new wave of momentum unfolds.
Key Considerations
While hidden divergences are valuable, they should not be used in isolation. Combining them with support/resistance levels, volume analysis, and price action confirmation enhances their reliability. Additionally, they tend to work best in strong trending markets, where momentum indicators align with price direction.
By mastering hidden divergences, traders can refine their ability to ride trends effectively, reducing the risk of exiting too soon or misinterpreting temporary pullbacks as reversals.
In my trading, I have used this indicator since 2009. My general aim is to make it available to all my friends. If you are using it, you are also my friend. So happy trading.
Momentum Flip Pro - Advanced ZigZag Trading SystemMomentum Flip Pro - Advanced ZigZag Trading System
Complete User Guide
📊 What This Indicator Does
The Momentum Flip Pro is an advanced position-flipping trading system that automatically identifies trend reversals using ZigZag patterns combined with momentum analysis. It's designed for traders who want to always be in the market, flipping between long and short positions at optimal reversal points.
Key Features:
Automatically flips positions at each ZigZag reversal point
Dynamic stop loss placement at exact ZigZag levels
Real-time trading dashboard with performance metrics
Capital tracking and ROI calculation
Three momentum engines to choose from
🎯 How It Works
Entry Signal: When a ZigZag point appears (circle on chart), the indicator:
Exits current position (if any)
Immediately enters opposite position
Places stop loss at the exact ZigZag price
Exit Signal: Positions are closed when the next ZigZag appears, then immediately reversed
Position Management:
Long Entry: ZigZag bottom (momentum turns UP)
Short Entry: ZigZag peak (momentum turns DOWN)
Stop Loss: Always at the ZigZag entry price
Take Profit: Next ZigZag point (automatic position flip)
⚙️ Recommended Settings
For Day Trading (5m-15m timeframes):
Momentum Engine: Quantum
- RSI Length: 9-12
- Quantum Factor: 3.5-4.0
- RSI Smoothing: 3-5
- Threshold: 8-10
For Swing Trading (1H-4H timeframes):
Momentum Engine: MACD
- Fast Length: 12
- Slow Length: 26
- Signal Smoothing: 9
- MA Type: EMA
For Position Trading (Daily):
Momentum Engine: Moving Average
- Average Type: EMA or HMA
- Length: 20-50
📈 How to Use for Trading
Add to Chart:
Add indicator to your chart
Set your starting capital
Choose your preferred momentum engine
Understanding Signals:
Green circles: Strong bullish momentum reversal
Red circles: Strong bearish momentum reversal
Purple circles: Normal momentum reversal
Entry labels: Show exact entry points with tooltips
Trading Rules:
Enter LONG when you see an up arrow + green/purple circle
Enter SHORT when you see a down arrow + red/purple circle
Stop loss is automatically at the ZigZag level
Hold until next ZigZag appears (exit + reverse)
Risk Management:
Risk per trade = Entry Price - Stop Loss
Position size = (Capital * Risk %) / Risk per trade
Recommended risk: 1-2% per trade
💡 Best Practices
Market Conditions:
Works best in trending markets
Excellent for volatile pairs (crypto, forex majors)
Avoid during low volume/consolidation
Timeframe Selection:
Lower timeframes (5m-15m): More signals, higher noise
Higher timeframes (1H+): Fewer signals, higher reliability
Sweet spot: 15m-1H for most traders
Momentum Engine Selection:
Quantum: Best for volatile markets (crypto, indices)
MACD: Best for trending markets (forex, stocks)
Moving Average: Best for smooth trends (commodities)
📊 Dashboard Interpretation
The trading dashboard shows:
Current Capital: Your running balance
Position: Current trade direction
Entry/Stop: Your risk levels
Statistics: Win rate and performance
ROI: Overall return on investment
⚠️ Important Notes
Always Active: This system is always in a position (long or short)
No Neutral: You're either long or short, never flat
Automatic Reversal: Positions flip at each signal
Stop Loss: Fixed at entry ZigZag level (doesn't trail)
🎮 Quick Start Guide
Beginners: Start with default settings on 1H timeframe
Test First: Use paper trading to understand the signals
Small Size: Begin with 1% risk per trade
Track Results: Monitor the dashboard statistics
Adjust: Fine-tune momentum settings based on results
🔧 Customization Tips
Color Signals: Enable to see momentum strength
Dashboard Position: Move to preferred screen location
Visual Settings: Adjust colors for your theme
Alerts: Set up for automated notifications
This indicator is ideal for traders who prefer an always-in-market approach with clear entry/exit rules and automated position management. The key to success is choosing the right momentum engine for your market and maintaining disciplined risk management.
RSI+SMI-WavesIndicator Description: RSI+SMI-Waves
RSI+SMI-Waves is not just a simple (mashup) of indicators — it's an advanced technical analysis tool, designed for traders who seek deeper and more precise insights, going beyond the traditional.
Pro Tip: To get the most out of this indicator and achieve a cleaner view, disable the price scale labels and line status values. You can also turn off the input values from the status line. This significantly improves visual clarity, making both SMI and RSI easier to read, without clutter.
This indicator combines two powerful oscillators — the Stochastic RSI and the SMI Ergodic — working in perfect harmony, like parts of a coordinated body: the RSI acts as the arm initiating movement, while the SMI is the head that validates and confirms the signal. This synergy avoids blind spots in market interpretation, making each slope or crossover meaningful for attentive traders.
The main advantage lies in the application of smoothed moving averages to all lines simultaneously. This reduces noise, reinforces real trends, and decreases the likelihood of false signals.
Indicator Structure
SMI Lines:
Purple: Long-period SMI (20)
Green: Short-period SMI (5)
Red: SMI signal line (also short period 5)
Stochastic RSI Lines:
Light Blue: RSI %K (base period 14, smoothed with 3 and 3)
Orange: RSI %D (signal line, same smoothing 3 and 3)
Levels and Zones
Dashed horizontal lines mark overbought and oversold zones for both RSI and SMI.
These zones are fully adjustable to suit the trader’s personal strategy or preferences.
How to Use
SMI vs RSI Crossovers: When the short-period SMI crosses above the RSI, it signals a bullish trend. When it crosses below, it indicates a bearish trend.
Moving Average Alignment: If all lines slope in the same direction, it confirms a stronger and more reliable signal.
Extreme Zones: Overbought/oversold levels are useful to anticipate possible trend reversals.
Dynamic Configuration: The trader can select the moving average type (EMA, HMA, JMA, etc.) and customize periods according to their strategy.
Market Arterial PressureIndicator Description: Pulse-Market – Market Blood Pressure
"I slept and had a dream."
In that dream, I wore a white lab coat and shiny black pointed shoes. I felt like a doctor—not of traditional medicine, but of the financial market itself. My mission was clear: to measure the market's blood pressure and diagnose its health.
With this vision, I decided to turn the dream into code. Thus, Pulse-Market was born: an indicator designed to listen to the heartbeat of the blockchain, capturing signs of vitality or collapse, and anticipating the pulse of the next trend.
But the journey did not stop there. At the core of this creation, I incorporated a profound theory: the cycle of existence — Alpha, Beta, and Omega — concepts that resonate both in science and sacred scriptures.
Alpha (α) represents the beginning: the primary impulse, the market's accelerated pulse.
Beta (β) symbolizes the middle: the vital rhythm, the stabilizing cadence of prices.
Omega (Ω) indicates the end: structural collapse, the exhaustion of a cycle.
This logical and symbolic triad forms the foundation of Pulse-Market — the beginning, middle, and end of every market cycle.
How to Use the Indicator
Pulse-Market works as a dynamic oscillator composed of three main forces:
Alpha Pulse (α)
Measures recent price acceleration. The stronger the pulse, the more intense the market movement.
Beta Rhythm (β)
Controls the smoothing of the price rhythm and can be adjusted in four modes:
Fast – quick reactions with more sensitivity
Normal – standard smoothing (simple moving average)
Slow – slow and consistent movements
Accelerated – Hull method: reactive and smooth
Omega Collapse (Ω)
Combines entropy and reversals to detect structural collapses where the market may be losing strength.
Visual Interpretation
Green line above zero: healthy pulse, buying pressure in control.
Red line below zero: strong selling pressure, possible exhaustion.
Crossing the zero line: potential trend reversal.
Settings and Customization
In the indicator settings panel, you can calibrate the pressure reading sensitivity:
Systolic Pressure (α): controls the reaction to rapid price impulses.
Increase to highlight aggressive moves; decrease to smooth spikes.
Diastolic Pressure (β): regulates the importance of the underlying rhythm.
Increase for smooth trends; decrease for quicker responses.
Pulse Pressure (Ω): sensitivity to structural collapses and volatility.
Increase to detect reversals; decrease to ignore market noise.
Practical Applications
Confirm entry and exit signals based on the balance between Alpha and Omega.
Adjust the indicator to your trading style: scalper, day trader, or swing trader.
Use on any asset: cryptocurrencies, stocks, indices, forex.
Integrated Philosophy
We live limited by time and matter, but markets, like life, follow natural cycles: they are born, mature, collapse, and are reborn.
Pulse-Market is not just a technical indicator — it is a spiritual and analytical stethoscope that listens to the heartbeat of volatility and tries to anticipate what the eyes cannot see, but time always reveals.
Original Creator
This indicator was created by Canhoto-Medium, the sole inventor and namer of this tool. As long as time goes on, no other indicator will exist with this essence or name.
Market Pulse ProMarket Pulse Pro (Pulse‑X) — User Guide
Market Pulse Pro, also known as Pulse‑X, is an advanced momentum indicator that combines SMI, Stochastic RSI, and a smoothed signal line to identify zones of buying and selling strength in the market. It is designed to assess the balance of power between bulls and bears with clear visualizations.
How It Works
The indicator calculates three main components:
SMI (Stochastic Momentum Index) – measures price position relative to its recent range.
Stochastic RSI – captures overbought/oversold extremes of the RSI.
Smoothed Signal Line – based on closing price, smoothed using various methods (such as HMA, EMA, etc.).
Each component is normalized to create two final values:
Bull Herd (Buying Strength) – green line.
Bear Winter (Selling Strength) – red line.
Interpretation
Bull Herd (high green values): Bulls dominate the market. May indicate the start or continuation of an uptrend.
Bear Winter (high red values): Bears dominate. May indicate reversal or continuation of a downtrend.
Convergence around 50%: Market is balanced. Signals are weaker or indecisive.
Tip: Combine with price action analysis or support/resistance levels to confirm entries.
Customizable Settings
You can adjust:
SMI Period, Smooth K, and D – control the sensitivity of the SMI.
RSI Period – sets the RSI calculation window.
Signal Period – period for the price-based signal line.
Smoothing Methods – choose between HMA, EMA, WMA, JMA, SMMA, etc.
Line Width – thickness of the plotted lines.
Note: The JMA (Jurik Moving Average) used in this script is not the original proprietary version.
It is a custom public version, based on open-source code shared by the TradingView community.
The original JMA is copyrighted and owned by Jurik Research.
How to Use It in Practice
Buy Entries
When the green Bull Herd line crosses above 60 and the red Bear Winter line falls below 40.
Entry is more reliable if the green line is rising steadily.
Sell Entries
When the red Bear Winter line crosses above 60 and the green Bull Herd line falls.
Signals are stronger when there is a clear crossover and divergence between the two lines.
Avoid trading near the neutral zone (~50%), where the market shows indecision.
Additional Tips
Combine with volume analysis or reversal candlestick patterns for higher accuracy.
Test different smoothing methods: HMA is more responsive, SMMA is smoother and slower.
Fear-Greed ThermometerFear-Greed Thermometer
This indicator measures market sentiment between fear and greed by combining three key factors: volatility, average volume, and percentage price change. Each factor is normalized and averaged to produce an index ranging from 0 to 100 that reflects the overall level of market fear or greed.
How to use:
Index above 50: Indicates greed dominance. The market tends to be more optimistic, signaling potential bullish conditions or overbought levels.
Index below 50: Indicates fear dominance. The market is more cautious or pessimistic, pointing to potential bearish conditions or oversold levels.
Neutral line (50): Acts as a reference for transitions between fear and greed phases.
Features:
Dynamic background: The chart background changes color according to sentiment — green for greed, red for fear — making it easy to visually gauge the index.
Customizable: Adjust the calculation periods for volatility, volume, and price change to fit your analysis style.
Tips:
Use alongside other technical tools to confirm entry and exit points.
Watch for divergences between the index and price to anticipate possible reversals.
Monitoring extreme levels can help identify market turning points.
This indicator is not a buy or sell recommendation but an additional tool to help understand the overall market sentiment.
15-Metric Strong Buy System (Final Version)This Pine Script analyzes 15 key technical signals to identify strong buy opportunities.
It assigns a score out of 13 for core conditions and checks 2 optional filters (Delivery Spike and RS > Nifty).
It only shows signals on the latest bar to keep the chart clean.
🧠 Core Structure Breakdown
✅ SECTION 1: Core Technical Signals (13 total)
Each of the following conditions adds 1 point to the score:
# Signal What It Checks Why It's Used
1 RSI (Daily & 1H) Both timeframes >55 and rising Confirms strength across multiple timeframes
2 Price > EMA20 & EMA50 Above short/medium-term trend lines Confirms uptrend support
3 Price near 20-day High Within 2% of recent high Breakout proximity
4 Volume > 20 SMA Unusual trading activity Confirms strength behind move
5 MACD Bullish Line > Signal and Histogram > 0 Momentum is building
6 ADX > 20 and +DI > -DI Trend strength and direction Validates strong directional trend
7 Bullish Candle Green candle with higher close Confirms bullish price action
8 BB Breakout Close > Upper Bollinger Band Volatility breakout confirmation
9 Supertrend Proxy Close above custom supertrend Simple trend direction filter
10 Close > Prev. High Break of short-term resistance Momentum continuation
11 VWAP Filter Close above VWAP Price leadership relative to volume
12 Weekly RSI > 50 Long-term trend momentum Confirms broader trend strength
13 BB Squeeze Bollinger Band width is low Signals potential upcoming move
🎯 Total Score = 0 to 13
🎯 SECTION 2: Optional Strength Filters (Displayed as labels)
Label What It Means Why It Matters
RS > Nifty Relative strength RSI > 55 vs Nifty Indicates outperformance
Delivery Spike Volume > 1.5× avg & bullish candle Suggests institutional activity
These are not included in the score, but if triggered, the chart displays:
🏷️ "RS > NIFTY" and/or
🏷️ "DELIVERY SPIKE"
in black background + white text boxes above the latest bar.
✅ SECTION 3: Output Logic
If score ≥ 12 → Show "STRONG BUY" on latest candle
If score = 11 → Show "BUY"
Also shows score label like Score: 12/13
Optional filters are displayed as labels if true
No clutter on historical candles — only current signal is shown.
🔔 SECTION 4: Alerts
Alert Trigger What It Does
score >= 12 Fires "Strong Buy Alert"
score == 11 Fires "Buy Alert"
⚖️ Pros & Cons (Analyst View)
✅ Pros:
Covers breadth of momentum, trend, breakout, and strength
Uses multi-timeframe confirmation for reliability
Avoids repainting by using latest bar only
Optional filters help detect smart money participation
Clean, uncluttered interface
❌ Cons:
Doesn’t include fundamentals
False positives possible during sideways or news-driven moves
No exit logic or Sell trigger
Uses proxy for Delivery spike, not actual delivery % (due to TradingView data limits)
💡 When to Trust the Signal?
Score Label Confidence
13/13 ✅ STRONG BUY + optional filters 🔥 Extremely strong
12/13 ✅ STRONG BUY ✅ Very high
11/13 BUY ⚠️ Medium — confirm with chart pattern
<11 — ❌ Avoid
Risk-Adjusted Momentum Oscillator# Risk-Adjusted Momentum Oscillator (RAMO): Momentum Analysis with Integrated Risk Assessment
## 1. Introduction
Momentum indicators have been fundamental tools in technical analysis since the pioneering work of Wilder (1978) and continue to play crucial roles in systematic trading strategies (Jegadeesh & Titman, 1993). However, traditional momentum oscillators suffer from a critical limitation: they fail to account for the risk context in which momentum signals occur. This oversight can lead to significant drawdowns during periods of market stress, as documented extensively in the behavioral finance literature (Kahneman & Tversky, 1979; Shefrin & Statman, 1985).
The Risk-Adjusted Momentum Oscillator addresses this gap by incorporating real-time drawdown metrics into momentum calculations, creating a self-regulating system that automatically adjusts signal sensitivity based on current risk conditions. This approach aligns with modern portfolio theory's emphasis on risk-adjusted returns (Markowitz, 1952) and reflects the sophisticated risk management practices employed by institutional investors (Ang, 2014).
## 2. Theoretical Foundation
### 2.1 Momentum Theory and Market Anomalies
The momentum effect, first systematically documented by Jegadeesh & Titman (1993), represents one of the most robust anomalies in financial markets. Subsequent research has confirmed momentum's persistence across various asset classes, time horizons, and geographic markets (Fama & French, 1996; Asness, Moskowitz & Pedersen, 2013). However, momentum strategies are characterized by significant time-varying risk, with particularly severe drawdowns during market reversals (Barroso & Santa-Clara, 2015).
### 2.2 Drawdown Analysis and Risk Management
Maximum drawdown, defined as the peak-to-trough decline in portfolio value, serves as a critical risk metric in professional portfolio management (Calmar, 1991). Research by Chekhlov, Uryasev & Zabarankin (2005) demonstrates that drawdown-based risk measures provide superior downside protection compared to traditional volatility metrics. The integration of drawdown analysis into momentum calculations represents a natural evolution toward more sophisticated risk-aware indicators.
### 2.3 Adaptive Smoothing and Market Regimes
The concept of adaptive smoothing in technical analysis draws from the broader literature on regime-switching models in finance (Hamilton, 1989). Perry Kaufman's Adaptive Moving Average (1995) pioneered the application of efficiency ratios to adjust indicator responsiveness based on market conditions. RAMO extends this concept by incorporating volatility-based adaptive smoothing, allowing the indicator to respond more quickly during high-volatility periods while maintaining stability during quiet markets.
## 3. Methodology
### 3.1 Core Algorithm Design
The RAMO algorithm consists of several interconnected components:
#### 3.1.1 Risk-Adjusted Momentum Calculation
The fundamental innovation of RAMO lies in its risk adjustment mechanism:
Risk_Factor = 1 - (Current_Drawdown / Maximum_Drawdown × Scaling_Factor)
Risk_Adjusted_Momentum = Raw_Momentum × max(Risk_Factor, 0.05)
This formulation ensures that momentum signals are dampened during periods of high drawdown relative to historical maximums, implementing an automatic risk management overlay as advocated by modern portfolio theory (Markowitz, 1952).
#### 3.1.2 Multi-Algorithm Momentum Framework
RAMO supports three distinct momentum calculation methods:
1. Rate of Change: Traditional percentage-based momentum (Pring, 2002)
2. Price Momentum: Absolute price differences
3. Log Returns: Logarithmic returns preferred for volatile assets (Campbell, Lo & MacKinlay, 1997)
This multi-algorithm approach accommodates different asset characteristics and volatility profiles, addressing the heterogeneity documented in cross-sectional momentum studies (Asness et al., 2013).
### 3.2 Leading Indicator Components
#### 3.2.1 Momentum Acceleration Analysis
The momentum acceleration component calculates the second derivative of momentum, providing early signals of trend changes:
Momentum_Acceleration = EMA(Momentum_t - Momentum_{t-n}, n)
This approach draws from the physics concept of acceleration and has been applied successfully in financial time series analysis (Treadway, 1969).
#### 3.2.2 Linear Regression Prediction
RAMO incorporates linear regression-based prediction to project momentum values forward:
Predicted_Momentum = LinReg_Value + (LinReg_Slope × Forward_Offset)
This predictive component aligns with the literature on technical analysis forecasting (Lo, Mamaysky & Wang, 2000) and provides leading signals for trend changes.
#### 3.2.3 Volume-Based Exhaustion Detection
The exhaustion detection algorithm identifies potential reversal points by analyzing the relationship between momentum extremes and volume patterns:
Exhaustion = |Momentum| > Threshold AND Volume < SMA(Volume, 20)
This approach reflects the established principle that sustainable price movements require volume confirmation (Granville, 1963; Arms, 1989).
### 3.3 Statistical Normalization and Robustness
RAMO employs Z-score normalization with outlier protection to ensure statistical robustness:
Z_Score = (Value - Mean) / Standard_Deviation
Normalized_Value = max(-3.5, min(3.5, Z_Score))
This normalization approach follows best practices in quantitative finance for handling extreme observations (Taleb, 2007) and ensures consistent signal interpretation across different market conditions.
### 3.4 Adaptive Threshold Calculation
Dynamic thresholds are calculated using Bollinger Band methodology (Bollinger, 1992):
Upper_Threshold = Mean + (Multiplier × Standard_Deviation)
Lower_Threshold = Mean - (Multiplier × Standard_Deviation)
This adaptive approach ensures that signal thresholds adjust to changing market volatility, addressing the critique of fixed thresholds in technical analysis (Taylor & Allen, 1992).
## 4. Implementation Details
### 4.1 Adaptive Smoothing Algorithm
The adaptive smoothing mechanism adjusts the exponential moving average alpha parameter based on market volatility:
Volatility_Percentile = Percentrank(Volatility, 100)
Adaptive_Alpha = Min_Alpha + ((Max_Alpha - Min_Alpha) × Volatility_Percentile / 100)
This approach ensures faster response during volatile periods while maintaining smoothness during stable conditions, implementing the adaptive efficiency concept pioneered by Kaufman (1995).
### 4.2 Risk Environment Classification
RAMO classifies market conditions into three risk environments:
- Low Risk: Current_DD < 30% × Max_DD
- Medium Risk: 30% × Max_DD ≤ Current_DD < 70% × Max_DD
- High Risk: Current_DD ≥ 70% × Max_DD
This classification system enables conditional signal generation, with long signals filtered during high-risk periods—a approach consistent with institutional risk management practices (Ang, 2014).
## 5. Signal Generation and Interpretation
### 5.1 Entry Signal Logic
RAMO generates enhanced entry signals through multiple confirmation layers:
1. Primary Signal: Crossover between indicator and signal line
2. Risk Filter: Confirmation of favorable risk environment for long positions
3. Leading Component: Early warning signals via acceleration analysis
4. Exhaustion Filter: Volume-based reversal detection
This multi-layered approach addresses the false signal problem common in traditional technical indicators (Brock, Lakonishok & LeBaron, 1992).
### 5.2 Divergence Analysis
RAMO incorporates both traditional and leading divergence detection:
- Traditional Divergence: Price and indicator divergence over 3-5 periods
- Slope Divergence: Momentum slope versus price direction
- Acceleration Divergence: Changes in momentum acceleration
This comprehensive divergence analysis framework draws from Elliott Wave theory (Prechter & Frost, 1978) and momentum divergence literature (Murphy, 1999).
## 6. Empirical Advantages and Applications
### 6.1 Risk-Adjusted Performance
The risk adjustment mechanism addresses the fundamental criticism of momentum strategies: their tendency to experience severe drawdowns during market reversals (Daniel & Moskowitz, 2016). By automatically reducing position sizing during high-drawdown periods, RAMO implements a form of dynamic hedging consistent with portfolio insurance concepts (Leland, 1980).
### 6.2 Regime Awareness
RAMO's adaptive components enable regime-aware signal generation, addressing the regime-switching behavior documented in financial markets (Hamilton, 1989; Guidolin, 2011). The indicator automatically adjusts its parameters based on market volatility and risk conditions, providing more reliable signals across different market environments.
### 6.3 Institutional Applications
The sophisticated risk management overlay makes RAMO particularly suitable for institutional applications where drawdown control is paramount. The indicator's design philosophy aligns with the risk budgeting approaches used by hedge funds and institutional investors (Roncalli, 2013).
## 7. Limitations and Future Research
### 7.1 Parameter Sensitivity
Like all technical indicators, RAMO's performance depends on parameter selection. While default parameters are optimized for broad market applications, asset-specific calibration may enhance performance. Future research should examine optimal parameter selection across different asset classes and market conditions.
### 7.2 Market Microstructure Considerations
RAMO's effectiveness may vary across different market microstructure environments. High-frequency trading and algorithmic market making have fundamentally altered market dynamics (Aldridge, 2013), potentially affecting momentum indicator performance.
### 7.3 Transaction Cost Integration
Future enhancements could incorporate transaction cost analysis to provide net-return-based signals, addressing the implementation shortfall documented in practical momentum strategy applications (Korajczyk & Sadka, 2004).
## References
Aldridge, I. (2013). *High-Frequency Trading: A Practical Guide to Algorithmic Strategies and Trading Systems*. 2nd ed. Hoboken, NJ: John Wiley & Sons.
Ang, A. (2014). *Asset Management: A Systematic Approach to Factor Investing*. New York: Oxford University Press.
Arms, R. W. (1989). *The Arms Index (TRIN): An Introduction to the Volume Analysis of Stock and Bond Markets*. Homewood, IL: Dow Jones-Irwin.
Asness, C. S., Moskowitz, T. J., & Pedersen, L. H. (2013). Value and momentum everywhere. *Journal of Finance*, 68(3), 929-985.
Barroso, P., & Santa-Clara, P. (2015). Momentum has its moments. *Journal of Financial Economics*, 116(1), 111-120.
Bollinger, J. (1992). *Bollinger on Bollinger Bands*. New York: McGraw-Hill.
Brock, W., Lakonishok, J., & LeBaron, B. (1992). Simple technical trading rules and the stochastic properties of stock returns. *Journal of Finance*, 47(5), 1731-1764.
Calmar, T. (1991). The Calmar ratio: A smoother tool. *Futures*, 20(1), 40.
Campbell, J. Y., Lo, A. W., & MacKinlay, A. C. (1997). *The Econometrics of Financial Markets*. Princeton, NJ: Princeton University Press.
Chekhlov, A., Uryasev, S., & Zabarankin, M. (2005). Drawdown measure in portfolio optimization. *International Journal of Theoretical and Applied Finance*, 8(1), 13-58.
Daniel, K., & Moskowitz, T. J. (2016). Momentum crashes. *Journal of Financial Economics*, 122(2), 221-247.
Fama, E. F., & French, K. R. (1996). Multifactor explanations of asset pricing anomalies. *Journal of Finance*, 51(1), 55-84.
Granville, J. E. (1963). *Granville's New Key to Stock Market Profits*. Englewood Cliffs, NJ: Prentice-Hall.
Guidolin, M. (2011). Markov switching models in empirical finance. In D. N. Drukker (Ed.), *Missing Data Methods: Time-Series Methods and Applications* (pp. 1-86). Bingley: Emerald Group Publishing.
Hamilton, J. D. (1989). A new approach to the economic analysis of nonstationary time series and the business cycle. *Econometrica*, 57(2), 357-384.
Jegadeesh, N., & Titman, S. (1993). Returns to buying winners and selling losers: Implications for stock market efficiency. *Journal of Finance*, 48(1), 65-91.
Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. *Econometrica*, 47(2), 263-291.
Kaufman, P. J. (1995). *Smarter Trading: Improving Performance in Changing Markets*. New York: McGraw-Hill.
Korajczyk, R. A., & Sadka, R. (2004). Are momentum profits robust to trading costs? *Journal of Finance*, 59(3), 1039-1082.
Leland, H. E. (1980). Who should buy portfolio insurance? *Journal of Finance*, 35(2), 581-594.
Lo, A. W., Mamaysky, H., & Wang, J. (2000). Foundations of technical analysis: Computational algorithms, statistical inference, and empirical implementation. *Journal of Finance*, 55(4), 1705-1765.
Markowitz, H. (1952). Portfolio selection. *Journal of Finance*, 7(1), 77-91.
Murphy, J. J. (1999). *Technical Analysis of the Financial Markets: A Comprehensive Guide to Trading Methods and Applications*. New York: New York Institute of Finance.
Prechter, R. R., & Frost, A. J. (1978). *Elliott Wave Principle: Key to Market Behavior*. Gainesville, GA: New Classics Library.
Pring, M. J. (2002). *Technical Analysis Explained: The Successful Investor's Guide to Spotting Investment Trends and Turning Points*. 4th ed. New York: McGraw-Hill.
Roncalli, T. (2013). *Introduction to Risk Parity and Budgeting*. Boca Raton, FL: CRC Press.
Shefrin, H., & Statman, M. (1985). The disposition to sell winners too early and ride losers too long: Theory and evidence. *Journal of Finance*, 40(3), 777-790.
Taleb, N. N. (2007). *The Black Swan: The Impact of the Highly Improbable*. New York: Random House.
Taylor, M. P., & Allen, H. (1992). The use of technical analysis in the foreign exchange market. *Journal of International Money and Finance*, 11(3), 304-314.
Treadway, A. B. (1969). On rational entrepreneurial behavior and the demand for investment. *Review of Economic Studies*, 36(2), 227-239.
Wilder, J. W. (1978). *New Concepts in Technical Trading Systems*. Greensboro, NC: Trend Research.
Cumulative Intraday Volume with Long/Short LabelsThis indicator calculates a running total of volume for each trading day, then shows on the price chart when that total crosses levels you choose. Every day at 6:00 PM Eastern Time, the total goes back to zero so it always reflects only the current day’s activity. From that moment on, each time a new candle appears the indicator looks at whether the candle closed higher than it opened or lower. If it closed higher, the candle’s volume is added to the running total; if it closed lower, the same volume amount is subtracted. As a result, the total becomes positive when buyers have dominated so far today and negative when sellers have dominated.
Because futures markets close at 6 PM ET, the running total resets exactly then, mirroring the way most intraday traders think in terms of a single session. Throughout the day, you will see this running total move up or down according to whether more volume is happening on green or red candles. Once the total goes above a number you specify (for example, one hundred thousand contracts), the indicator will place a small “Long” label at that candle on the main price chart to let you know buying pressure has reached that level. Similarly, once the total goes below a negative number you choose (for example, minus one hundred thousand), a “Short” label will appear at that candle to signal that selling pressure has reached your chosen threshold. You can set these threshold numbers to whatever makes sense for your trading style or the market you follow.
Because raw volume alone never turns negative, this design uses candle direction as a sign. Green candles (where the close is higher than the open) add volume, and red candles (where the close is lower than the open) subtract volume. Summing those signed volume values tells you in a single number whether buying or selling has been stronger so far today. That number resets every evening, so it does not carry over any buying or selling from previous sessions.
Once you have this indicator on your chart, you simply watch the “summed volume” line as it moves throughout the day. If it climbs past your long threshold, you know buyers are firmly in control and a long entry might make sense. If it falls past your short threshold, you know sellers are firmly in control and a short entry might make sense. In quieter markets or times of low volume, you might use a smaller threshold so that even modest buying or selling pressure will trigger a label. During very active periods, a larger threshold will prevent too many signals when volume spikes frequently.
This approach is straightforward but can be surprisingly powerful. It does not rely on complex formulas or hidden statistical measures. Instead, it simply adds and subtracts daily volume based on candle color, then alerts you when that total reaches levels you care about. Over several years of historical testing, this formula has shown an ability to highlight moments when intraday sentiment shifts decisively from buyers to sellers or vice versa. Because the indicator resets every day at 6 PM, it always reflects only today’s sentiment and remains easy to interpret without carrying over past data. You can use it on any intraday timeframe, but it works especially well on five-minute or fifteen-minute charts for futures contracts.
If you want a clear gauge of whether buyers or sellers are dominating in real time, and you prefer a rule-based method rather than a complex model, this indicator gives you exactly that. It shows net buying or selling pressure at a glance, resets each session like most intraday traders do, and marks the moments when that pressure crosses the levels you decide are important. By combining a daily reset with signed volume, you get a single number that tells you precisely what the crowd is doing at any given moment, without any of the guesswork or hidden calculations that more complicated indicators often carry.
OA - SMESSmart Money Entry Signals (SMES)
The SMES indicator is developed to identify potential turning points in market behavior by analyzing internal price dynamics, rather than relying on external volume or sentiment data. It leverages normalized price movement, directional volatility, and smoothing algorithms to detect potential areas of accumulation or distribution by market participants.
Core Concepts
Smart Money Flow calculation based on normalized price positioning
Directional VHF (Vertical Horizontal Filter) used to enhance signal directionality
Overbought and Oversold regions defined with optional glow visualization
Entry and Exit signals based on dynamic crossovers
Highly customizable input parameters for precision control
Key Inputs
Smart Money Flow Period
Smoothing Period
Price Analysis Length
Fibonacci Lookback Length
Visual toggle options (zones, glow effects, signal display)
Usage
This tool plots the smoothed smart money flow as a standalone oscillator, designed to help traders identify potential momentum shifts or extremes in market sentiment. Entry signals are generated through crossover logic, while optional filters based on price behavior can refine those signals. Exit signals are shown when the smart money line exits extreme regions.
Important Notes
This indicator does not repaint
Works on all timeframes and instruments
Best used as a confirmation tool with other technical frameworks
All calculations are based strictly on price data
Disclaimer
This script is intended for educational purposes only. It does not provide financial advice or guarantee performance. Please do your own research and apply appropriate risk management before making any trading decisions.
Kaufman Trend Strategy# ✅ Kaufman Trend Strategy – Full Description (Script Publishing Version)
**Kaufman Trend Strategy** is a dynamic trend-following strategy based on Kaufman Filter theory.
It detects real-time trend momentum, reduces noise, and aims to enhance entry accuracy while optimizing risk.
⚠️ _For educational and research purposes only. Past performance does not guarantee future results._
---
## 🎯 Strategy Objective
- Smooth price noise using Kaufman Filter smoothing
- Detect the strength and direction of trends with a normalized oscillator
- Manage profits using multi-stage take-profits and adaptive ATR stop-loss logic
---
## ✨ Key Features
- **Kaufman Filter Trend Detection**
Extracts directional signal using a state space model.
- **Multi-Stage Profit-Taking**
Automatically takes partial profits based on color changes and zero-cross events.
- **ATR-Based Volatility Stops**
Stops adjust based on swing highs/lows and current market volatility.
---
## 📊 Entry & Exit Logic
**Long Entry**
- `trend_strength ≥ 60`
- Green trend signal
- Price above the Kaufman average
**Short Entry**
- `trend_strength ≤ -60`
- Red trend signal
- Price below the Kaufman average
**Exit (Long/Short)**
- Blue trend color → TP1 (50%)
- Oscillator crosses 0 → TP2 (25%)
- Trend weakens → Final exit (25%)
- ATR + swing-based stop loss
---
## 💰 Risk Management
- Initial capital: `$3,000`
- Order size: `$100` per trade (realistic, low-risk sizing)
- Commission: `0.002%`
- Slippage: `2 ticks`
- Pyramiding: `1` max position
- Estimated risk/trade: `~0.1–0.5%` of equity
> ⚠️ _No trade risks more than 5% of equity. This strategy follows TradingView script publishing rules._
---
## ⚙️ Default Parameters
- **1st Take Profit**: 50%
- **2nd Take Profit**: 25%
- **Final Exit**: 25%
- **ATR Period**: 14
- **Swing Lookback**: 10
- **Entry Threshold**: ±60
- **Exit Threshold**: ±40
---
## 📅 Backtest Summary
- **Symbol**: USD/JPY
- **Timeframe**: 1H
- **Date Range**: Jan 3, 2022 – Jun 4, 2025
- **Trades**: 924
- **Win Rate**: 41.67%
- **Profit Factor**: 1.108
- **Net Profit**: +$1,659.29 (+54.56%)
- **Max Drawdown**: -$1,419.73 (-31.87%)
---
## ✅ Summary
This strategy uses Kaufman filtering to detect market direction with reduced lag and increased smoothness.
It’s built with visual clarity and strong trade management, making it practical for both beginners and advanced users.
---
## 📌 Disclaimer
This script is for educational and informational purposes only and should not be considered financial advice.
Use with proper risk controls and always test in a demo environment before live trading.
Uptrick: Z-Trend BandsOverview
Uptrick: Z-Trend Bands is a Pine Script overlay crafted to capture high-probability mean-reversion opportunities. It dynamically plots upper and lower statistical bands around an EMA baseline by converting price deviations into z-scores. Once price moves outside these bands and then reenters, the indicator verifies that momentum is genuinely reversing via an EMA-smoothed RSI slope. Signal memory ensures only one entry per momentum swing, and traders receive clear, real-time feedback through customizable bar-coloring modes, a semi-transparent fill highlighting the statistical zone, concise “Up”/“Down” labels, and a live five-metric scoring table.
Introduction
Markets often oscillate between trending and reverting, and simple thresholds or static envelopes frequently misfire when volatility shifts. Standard deviation quantifies how “wide” recent price moves have been, and a z-score transforms each deviation into a measure of how rare it is relative to its own history. By anchoring these bands to an exponential moving average, the script maintains a fluid statistical envelope that adapts instantly to both calm and turbulent regimes. Meanwhile, the Relative Strength Index (RSI) tracks momentum; smoothing RSI with an EMA and observing its slope filters out erratic spikes, ensuring that only genuine momentum flips—upward for longs and downward for shorts—qualify.
Purpose
This indicator is purpose-built for short-term mean-reversion traders operating on lower–timeframe charts. It reveals when price has strayed into the outer 5 percent of its recent range, signaling an increased likelihood of a bounce back toward fair value. Rather than firing on price alone, it demands that momentum follow suit: the smoothed RSI slope must flip in the opposite direction before any trade marker appears. This dual-filter approach dramatically reduces noise-driven, false setups. Traders then see immediate visual confirmation—bar colors that reflect the latest signal and age over time, clear entry labels, and an always-visible table of metric scores—so they can gauge both the validity and freshness of each signal at a glance.
Originality and Uniqueness
Uptrick: Z-Trend Bands stands apart from typical envelope or oscillator tools in four key ways. First, it employs fully normalized z-score bands, meaning ±2 always captures roughly the top and bottom 5 percent of moves, regardless of volatility regime. Second, it insists on two simultaneous conditions—price reentry into the bands and a confirming RSI slope flip—dramatically reducing whipsaw signals. Third, it uses slope-phase memory to lock out duplicate signals until momentum truly reverses again, enforcing disciplined entries. Finally, it offers four distinct bar-coloring schemes (solid reversal, fading reversal, exceeding bands, and classic heatmap) plus a dynamic scoring table, rather than a single, opaque alert, giving traders deep insight into every layer of analysis.
Why Each Component Was Picked
The EMA baseline was chosen for its blend of responsiveness—weighting recent price heavily—and smoothness, which filters market noise. Z-score deviation bands standardize price extremes relative to their own history, adapting automatically to shifting volatility so that “extreme” always means statistically rare. The RSI, smoothed with an EMA before slope calculation, captures true momentum shifts without the false spikes that raw RSI often produces. Slope-phase memory flags prevent repeated alerts within a single swing, curbing over-trading in choppy conditions. Bar-coloring modes provide flexible visual contexts—whether you prefer to track the latest reversal, see signal age, highlight every breakout, or view a continuous gradient—and the scoring table breaks down all five core checks for complete transparency.
Features
This indicator offers a suite of configurable visual and logical tools designed to make reversal signals both robust and transparent:
Dynamic z-score bands that expand or contract in real time to reflect current volatility regimes, ensuring the outer ±zThreshold levels always represent statistically rare extremes.
A smooth EMA baseline that weights recent price more heavily, serving as a fair-value anchor around which deviations are measured.
EMA-smoothed RSI slope confirmation, which filters out erratic momentum spikes by first smoothing raw RSI and then requiring its bar-to-bar slope to flip before any signal is allowed.
Slope-phase memory logic that locks out duplicate buy or sell markers until the RSI slope crosses back through zero, preventing over-trading during choppy swings.
Four distinct bar-coloring modes—Reversal Solid, Reversal Fade, Exceeding Bands, Classic Heat—plus a “None” option, so traders can choose whether to highlight the latest signal, show signal age, emphasize breakout bars, or view a continuous heat gradient within the bands.
A semi-transparent fill between the EMA and the upper/lower bands that visually frames the statistical zone and makes extremes immediately obvious.
Concise “Up” and “Down” labels that plot exactly when price re-enters a band with confirming momentum, keeping chart clutter to a minimum.
A real-time, five-metric scoring table (z-score, RSI slope, price vs. EMA, trend state, re-entry) that updates every two bars, displaying individual +1/–1/0 scores and an averaged Buy/Sell/Neutral verdict for complete transparency.
Calculations
Compute the fair-value EMA over fairLen bars.
Subtract that EMA from current price each bar to derive the raw deviation.
Over zLen bars, calculate the rolling mean and standard deviation of those deviations.
Convert each deviation into a z-score by subtracting the mean and dividing by the standard deviation.
Plot the upper and lower bands at ±zThreshold × standard deviation around the EMA.
Calculate raw RSI over rsiLen bars, then smooth it with an EMA of length rsiEmaLen.
Derive the RSI slope by taking the difference between the current and previous smoothed RSI.
Detect a potential reentry when price exits one of the bands on the prior bar and re-enters on the current bar.
Require that reentry coincide with an RSI slope flip (positive for a lower-band reentry, negative for an upper-band reentry).
On first valid reentry per momentum swing, fire a buy or sell signal and set a memory flag; reset that flag only when the RSI slope crosses back through zero.
For each bar, assign scores of +1, –1, or 0 for the z-score direction, RSI slope, price vs. EMA, trend-state, and reentry status.
Average those five scores; if the result exceeds +0.1, label “Buy,” if below –0.1, label “Sell,” otherwise “Neutral.”
Update bar colors, the semi-transparent fill, reversal labels, and the scoring table every two bars to reflect the latest calculations.
How It Actually Works
On each new candle, the EMA baseline and band widths update to reflect current volatility. The RSI is smoothed and its slope recalculated. The script then looks back one bar to see if price exited either band and forward to see if it reentered. If that reentry coincides with an appropriate RSI slope flip—and no signal has yet been generated in that swing—a concise label appears. Bar colors refresh according to your selected mode, and the scoring table updates to show which of the five conditions passed or failed, along with the overall verdict. This process repeats seamlessly at each bar, giving traders a continuous feed of disciplined, statistically filtered reversal cues.
Inputs
All parameters are fully user-configurable, allowing you to tailor sensitivity, lookbacks, and visuals to your trading style:
EMA length (fairLen): number of bars for the fair-value EMA; higher values smooth more but lag further behind price.
Z-Score lookback (zLen): window for calculating the mean and standard deviation of price deviations; longer lookbacks reduce noise but respond more slowly to new volatility.
Z-Score threshold (zThreshold): number of standard deviations defining the upper and lower bands; common default is 2.0 for roughly the outer 5 percent of moves.
Source (src): choice of price series (close, hl2, etc.) used for EMA, deviation, and RSI calculations.
RSI length (rsiLen): period for raw RSI calculation; shorter values react faster to momentum changes but can be choppier.
RSI EMA length (rsiEmaLen): period for smoothing raw RSI before taking its slope; higher values filter more noise.
Bar coloring mode (colorMode): select from None, Reversal Solid, Reversal Fade, Exceeding Bands, or Classic Heat to control how bars are shaded in relation to signals and band positions.
Show signals (showSignals): toggle on-chart “Up” and “Down” labels for reversal entries.
Show scoring table (enableTable): toggle the display of the five-metric breakdown table.
Table position (tablePos): choose which corner (Top Left, Top Right, Bottom Left, Bottom Right) hosts the scoring table.
Conclusion
By merging a normalized z-score framework, momentum slope confirmation, disciplined signal memory, flexible visuals, and transparent scoring into one Pine Script overlay, Uptrick: Z-Trend Bands offers a powerful yet intuitive tool for intraday mean-reversion trading. Its adaptability to real-time volatility and multi-layered filter logic deliver clear, high-confidence reversal cues without the clutter or confusion of simpler indicators.
Disclaimer
This indicator is provided solely for educational and informational purposes. It does not constitute financial advice. Trading involves substantial risk and may not be suitable for all investors. Past performance is not indicative of future results. Always conduct your own testing and apply careful risk management before trading live.
Triple EMA Momentum Oscillator (TEMO) HistogramThis Pine Script code replicates the Python indicator you provided, calculating the Triple EMA Momentum Oscillator (TEMO) and generating signals based on its value and momentum.
Explanation of the Code:
User Inputs:
Allows you to adjust the periods for the short, mid, and long EMAs.
Calculate EMAs:
Computes the Exponential Moving Averages for the specified periods.
Calculate EMA Spreads (Distances):
Finds the differences between the EMAs to understand the spread between them.
Calculate Spread Velocities:
Determines the change in spreads from the previous period, indicating momentum.
Composite Strength Score:
Weighted calculation of the spreads normalized by the EMA values.
Velocity Accelerator:
Weighted calculation of the velocities normalized by the EMA values.
Final TEMO Oscillator:
Combines the spread strength and velocity accelerator to create the TEMO.
Generate Signals:
Signals are generated when TEMO is positive and increasing (buy), or negative and decreasing (sell).
Plotting:
Zero Line: Helps visualize when TEMO crosses from positive to negative.
TEMO Oscillator: Plotted with green for positive values and red for negative values.
Signals: Displayed as a histogram to indicate buy (1) and sell (-1) signals.
Usage:
Buy Signal: When TEMO is above zero and increasing.
Sell Signal: When TEMO is below zero and decreasing.
Note: This oscillator helps identify momentum changes based on EMAs of different periods. It's useful for detecting trends and potential reversal points in the market.
Reversal Trap Sniper – Verified VersionReversal Trap Sniper
Overview
Reversal Trap Sniper is a counterintuitive momentum-following strategy that identifies "reversal traps"—situations where traders expect a market reversal based on RSI, but the price continues trending. By detecting these failed reversal signals, the strategy enters trades in the trend direction, often catching strong follow-through moves.
How It Works
The system monitors the Relative Strength Index (RSI). When RSI moves above the overbought level (e.g., 70) and then drops back below it, many traders interpret this as a sell signal.
However, this strategy treats such moves with caution. If the RSI pulls back below the overbought threshold but the price continues to rise, the system considers it a "reversal trap"—a fakeout.
In such cases, instead of going short, the strategy enters a long position, assuming that the trend is still valid and those betting on a reversal may fuel a breakout.
Similarly, if RSI rises above the oversold level from below, but price continues falling, a short trade is triggered.
Entries are followed by ATR-based stop-loss and dynamic take-profit (2× risk), with a fallback time-based exit after 30 bars.
Key Features
- Detects failed RSI-based reversals ("traps")
- Follows momentum after the trap is triggered
- Uses ATR for dynamic stop-loss and take-profit
- Auto-exit after a fixed bar count (30 bars)
- Visual markers on chart for transparency
- Realistic trading assumptions: 0.05% commission, slippage, and capped pyramiding
Parameter Explanation
RSI Length (14): Standard RSI calculation period
Overbought/Oversold Levels (70/30): Common thresholds used by many traders
ATR Length (14): Used to define stop-loss and target dynamically
Risk-Reward Ratio (2.0): Take-profit is set at 2× the stop-loss distance
Max Holding Bars (30): Ensures trades don’t remain open indefinitely
Pyramiding (10): Allows scaling into trades, simulating real-world strategy stacking
Originality Note
This strategy inverts traditional RSI logic. Instead of treating overbought/oversold conditions as signals for reversal, it waits for those signals to fail. Only after such failures, confirmed by continued price action in the same direction, does the system enter trades. This logic is based on the behavioral observation that failed reversal signals often trigger stronger trend continuation—making this strategy uniquely positioned to exploit trap scenarios.
Disclaimer
This script is for educational and research purposes only. Trading involves risk, and past performance does not guarantee future results. Always test thoroughly before applying with live capital.
Volatility Pulse with Dynamic ExitVolatility Pulse with Dynamic Exit
Overview
This strategy, Volatility Pulse with Dynamic Exit, is designed to capture impulsive price moves following volatility expansions, while ensuring risk is managed dynamically. It avoids trades during low-volatility periods and uses momentum confirmation to enter positions. Additionally, it features a time-based forced exit system to limit overexposure.
How It Works
A position is opened when the current ATR (Average True Range) significantly exceeds its 20-period average, signaling a volatility expansion.
To confirm the move is directional and not random noise, the strategy checks for momentum: the close must be above/below the close of 20 bars ago.
Low volatility zones are filtered out to avoid chop and poor trade entries.
Upon entry, a dynamic stop-loss is set at 1x ATR, while take-profit is set at 2x ATR, offering a 2:1 reward-to-risk ratio.
If the position remains open for more than 42 bars, it is forcefully closed, even if targets are not hit. This prevents long-lasting, stagnant trades.
Key Features
✅ Volatility-based breakout detection
✅ Momentum confirmation filter
✅ Dynamic stop-loss and take-profit based on real-time ATR
✅ Time-based forced exit (42 bars max holding)
✅ Low-volatility environment filter
✅ Realistic settings with 0.05% commission and slippage included
Parameters Explanation
ATR Length (14): Captures recent volatility over ~2 weeks (14 candles).
Momentum Lookback (20): Ensures meaningful price move confirmation.
Volatility Expansion Threshold (0.5x): Strategy activates only when ATR is at least 50% above its average.
Minimum ATR Filter (1.0x): Avoids entries in tight, compressed market ranges.
Max Holding (42 bars): Trades are closed after 42 bars if no exit signal is triggered.
Risk-Reward (2.0x): Aiming for 2x ATR as profit for every 1x ATR risk.
Originality Note
While volatility and momentum have been used separately in many strategies, this script combines both with a time-based dynamic exit system. This exit rule, combined with an ATR-based filter to exclude low-activity periods, gives the system a practical edge in real-world use. It avoids classic rehashes and integrates real trading constraints for better applicability.
Disclaimer
This is a research-focused trading strategy meant for backtesting and educational purposes. Always use proper risk management and perform due diligence before applying to real funds.
Dual Pwma Trends [ZORO_47]Key Features:
Dual PWMA System: Combines a fast and slow Parabolic Weighted Moving Average to identify momentum shifts and trend changes with precision.
Dynamic Color Coding: The indicator lines change color to reflect market conditions—green for bullish crossovers (potential buy signals) and red for bearish crossunders (potential sell signals), making it easy to interpret at a glance.
Customizable Parameters: Adjust the fast and slow PWMA lengths, power settings, and source data to tailor the indicator to your trading style and timeframe.
Clean Visualization: Plotted with bold, clear lines (3px width) for optimal visibility on any chart, ensuring you never miss a signal.
How It Works:
The indicator calculates two PWMAs using the imported ZOROLIBRARY by ZORO_47. When the fast PWMA crosses above the slow PWMA, both lines turn green, signaling a potential bullish trend. Conversely, when the fast PWMA crosses below the slow PWMA, the lines turn red, indicating a potential bearish trend. The color persists until the next crossover or crossunder, providing a seamless visual cue for trend direction.
Ideal For:
Trend Traders: Identify trend reversals and continuations with clear crossover signals.
Swing Traders: Use on higher timeframes to capture significant price moves.
Day Traders: Fine-tune settings for faster signals on intraday charts.
Settings:
Fast Length/Power: Control the sensitivity of the fast PWMA (default: 12/2).
Slow Length/Power: Adjust the smoother, slower PWMA (default: 21/1).
Source: Choose your preferred data input (default: close price).
EMA Pullback Speed Strategy 📌 **Overview**
The **EMA Pullback Speed Strategy** is a trend-following approach that combines **price momentum** and **Exponential Moving Averages (EMA)**.
It aims to identify high-probability entry points during brief pullbacks within ongoing uptrends or downtrends.
The strategy evaluates **speed of price movement**, **relative position to dynamic EMA**, and **candlestick patterns** to determine ideal timing for entries.
One of the key concepts is checking whether the price has **“not pulled back too much”**, helping focus only on situations where the trend is likely to continue.
⚠️ This strategy is designed for educational and research purposes only. It does not guarantee future profits.
🧭 **Purpose**
This strategy addresses the common issue of **"jumping in too late during trends and taking unnecessary losses."**
By waiting for a healthy pullback and confirming signs of **trend resumption**, traders can enter with greater confidence and reduce false entries.
🎯 **Strategy Objectives**
* Enter in the direction of the prevailing trend to increase win rate
* Filter out false signals using pullback depth, speed, and candlestick confirmations
* Predefine Take-Profit (TP) and Stop-Loss (SL) levels for safer, rule-based trading
✨ **Key Features**
* **Dynamic EMA**: Reacts faster when price moves quickly, slower when market is calm – adapting to current momentum
* **Pullback Filter**: Avoids trades when price pulls back too far (e.g., more than 5%), indicating a trend may be weakening
* **Speed Check**: Measures how strongly the price returns to the trend using candlestick body speed (open-to-close range in ticks)
📊 **Trading Rules**
**■ Long Entry Conditions:**
* Current price is above the dynamic EMA (indicating uptrend)
* Price has pulled back toward the EMA (a "buy the dip" situation)
* Pullback depth is within the threshold (not excessive)
* Candlesticks show consecutive bullish closes and break the previous high
* Price speed is strong (positive movement with momentum)
**■ Short Entry Conditions:**
* Current price is below the dynamic EMA (indicating downtrend)
* Price has pulled back up toward the EMA (a "sell the rally" setup)
* Pullback is within range (not too deep)
* Candlesticks show consecutive bearish closes and break the previous low
* Price speed is negative (downward momentum confirmed)
**■ Exit Conditions (TP/SL):**
* **Take-Profit (TP):** Fixed 1.5% target above/below entry price
* **Stop-Loss (SL):** Based on recent price volatility, calculated using ATR × 4
💰 **Risk Management Parameters**
* Symbol & Timeframe: BTCUSD on 1-hour chart (H1)
* Test Capital: \$3000 (simulated account)
* Commission: 0.02%
* Slippage: 2 ticks (minimal execution lag)
* Max risk per trade: 5% of account balance
* Backtest Period: Aug 30, 2023 – May 9, 2025
* Profit Factor (PF): 1.965 (Net profit ÷ Net loss, including spreads & fees)
⚙️ **Trading Parameters & Indicator Settings**
* Maximum EMA Length: 50
* Accelerator Multiplier: 3.0
* Pullback Threshold: 5.0%
* ATR Period: 14
* ATR Multiplier (SL distance): 4.0
* Fixed TP: 1.5%
* Short-term EMA: 21
* Long-term EMA: 50
* Long Speed Threshold: ≥ 1000.0 (ticks)
* Short Speed Threshold: ≤ -1000.0 (ticks)
⚠️Adjustments are based on BTCUSD.
⚠️Forex and other currency pairs require separate adjustments.
🔧 **Strategy Improvements & Uniqueness**
Unlike basic moving average crossovers or RSI triggers, this strategy emphasizes **"momentum-supported pullbacks"**.
By combining dynamic EMA, speed checks, and candlestick signals, it captures trades **as if surfing the wave of a trend.**
Its built-in filters help **avoid overextended pullbacks**, which often signal the trend is ending – making it more robust than traditional trend-following systems.
✅ **Summary**
The **EMA Pullback Speed Strategy** is easy to understand, rule-based, and highly reproducible – ideal for both beginners and intermediate traders.
Because it shows **clear visual entry/exit points** on the chart, it’s also a great tool for practicing discretionary trading decisions.
⚠️ Past performance is not a guarantee of future results.
Always respect your Stop-Loss levels and manage your position size according to your risk tolerance.
Fibo Normalized RSI & RSI RibbonPlots both standard and Z-score normalized RSI ribbons using Fibonacci-based periods. Supports adjustable normalization, optional 0–100 scaling, and multi-line visualizations for momentum and deviation analysis.
This tool is designed for traders who want to go beyond standard RSI by adding:
Statistical normalization (Z-score)
Multi-period analysis (Fibonacci structure)
Advanced divergence and exhaustion detection
It gives you both classical momentum context and mathematically rigorous deviation insight, making it ideal for:
Swing traders
Quant-inclined discretionary traders
Multi-timeframe analysts
Trend Confirmation
When both RSI and normalized RSI across short and long periods are stacked in the same direction (e.g., above 50 or with high Z-scores), the trend is likely strong.
Disagreement between the two ribbons (e.g., RSI high but normalized RSI flat) may indicate late-stage trend or false strength.
Mean Reversion Trades
Look for normalized RSI values > +2 or < -2 (i.e., ~2 standard deviations).
Cross-check with standard RSI to see if the move aligns with a traditional overbought/oversold level.
Great for fade/reversal setups when Z-score RSI is extreme but classic RSI is just beginning to turn.
Divergence Detection
Compare the slope of RSI vs. normalized RSI over same period:
If RSI is rising but normalized RSI is falling → momentum is fading despite apparent strength.
Excellent for early warnings before reversals.
Multi-Timeframe Confluence
Use short-period ribbons (e.g., 3–13) for tactical entries/exits.
Use long-period ribbons (e.g., 55–233) for macro trend bias.
Alignment across both = high-confidence zone.
