Ehlers Optimal Tracking FilterThe original script was posted on ProRealCode by user Nicolas.
Dr. R.E. Kalman introduced his concept of optimum estimation in 1960. Since that time, his technique has proven to be a powerful and practical tool. The approach is particularly well suited for optimizing the performance of modern terrestrial and space navigation systems. Many traders not directly involved in system analysis have heard about Kalman filtering and have expressed an interest in learning more about it for market applications. Although attempts have been made to provide simple, intuitive explanations, none has been completely successful. Almost without exception, descriptions have become mired in the jargon and state-space notation of the “cult”.
Surprisingly, in spite of the obscure-looking mathematics (the most impenetrable of which can be found in Dr. Kalman’s original paper), Kalman filtering is a fairly direct and simple concept. In the spirit of being pragmatic, we will not deal with the full-blown matrix equations in this description and we will be less than rigorous in the application to trading. Rigorous application requires knowledge of the probability distributions of the statistics. Nonetheless we end with practically useful results. We will depart from the classical approach by working backwards from Exponential Moving Averages. In this process, we introduce a way to create a nearly zero lag moving average. From there, we will use the concept of a Tracking Index that optimizes the filter tracking for the given uncertainty in price movement and the uncertainty in our ability to measure it.
Credits to: www.prorealcode.com
在腳本中搜尋"Exponential Moving Average"
Aetherium Institutional Market Resonance EngineAetherium Institutional Market Resonance Engine (AIMRE)
A Three-Pillar Framework for Decoding Institutional Activity
🎓 THEORETICAL FOUNDATION
The Aetherium Institutional Market Resonance Engine (AIMRE) is a multi-faceted analysis system designed to move beyond conventional indicators and decode the market's underlying structure as dictated by institutional capital flow. Its philosophy is built on a singular premise: significant market moves are preceded by a convergence of context , location , and timing . Aetherium quantifies these three dimensions through a revolutionary three-pillar architecture.
This system is not a simple combination of indicators; it is an integrated engine where each pillar's analysis feeds into a central logic core. A signal is only generated when all three pillars achieve a state of resonance, indicating a high-probability alignment between market organization, key liquidity levels, and cyclical momentum.
⚡ THE THREE-PILLAR ARCHITECTURE
1. 🌌 PILLAR I: THE COHERENCE ENGINE (THE 'CONTEXT')
Purpose: To measure the degree of organization within the market. This pillar answers the question: " Is the market acting with a unified purpose, or is it chaotic and random? "
Conceptual Framework: Institutional campaigns (accumulation or distribution) create a non-random, organized market environment. Retail-driven or directionless markets are characterized by "noise" and chaos. The Coherence Engine acts as a filter to ensure we only engage when institutional players are actively steering the market.
Formulaic Concept:
Coherence = f(Dominance, Synchronization)
Dominance Factor: Calculates the absolute difference between smoothed buying pressure (volume-weighted bullish candles) and smoothed selling pressure (volume-weighted bearish candles), normalized by total pressure. A high value signifies a clear winner between buyers and sellers.
Synchronization Factor: Measures the correlation between the streams of buying and selling pressure over the analysis window. A high positive correlation indicates synchronized, directional activity, while a negative correlation suggests choppy, conflicting action.
The final Coherence score (0-100) represents the percentage of market organization. A high score is a prerequisite for any signal, filtering out unpredictable market conditions.
2. 💎 PILLAR II: HARMONIC LIQUIDITY MATRIX (THE 'LOCATION')
Purpose: To identify and map high-impact institutional footprints. This pillar answers the question: " Where have institutions previously committed significant capital? "
Conceptual Framework: Large institutional orders leave indelible marks on the market in the form of anomalous volume spikes at specific price levels. These are not random occurrences but are areas of intense historical interest. The Harmonic Liquidity Matrix finds these footprints and consolidates them into actionable support and resistance zones called "Harmonic Nodes."
Algorithmic Process:
Footprint Identification: The engine scans the historical lookback period for candles where volume > average_volume * Institutional_Volume_Filter. This identifies statistically significant volume events.
Node Creation: A raw node is created at the mean price of the identified candle.
Dynamic Clustering: The engine uses an ATR-based proximity algorithm. If a new footprint is identified within Node_Clustering_Distance (ATR) of an existing Harmonic Node, it is merged. The node's price is volume-weighted, and its magnitude is increased. This prevents chart clutter and consolidates nearby institutional orders into a single, more significant level.
Node Decay: Nodes that are older than the Institutional_Liquidity_Scanback period are automatically removed from the chart, ensuring the analysis remains relevant to recent market dynamics.
3. 🌊 PILLAR III: CYCLICAL RESONANCE MATRIX (THE 'TIMING')
Purpose: To identify the market's dominant rhythm and its current phase. This pillar answers the question: " Is the market's immediate energy flowing up or down? "
Conceptual Framework: Markets move in waves and cycles of varying lengths. Trading in harmony with the current cyclical phase dramatically increases the probability of success. Aetherium employs a simplified wavelet analysis concept to decompose price action into short, medium, and long-term cycles.
Algorithmic Process:
Cycle Decomposition: The engine calculates three oscillators based on the difference between pairs of Exponential Moving Averages (e.g., EMA8-EMA13 for short cycle, EMA21-EMA34 for medium cycle).
Energy Measurement: The 'energy' of each cycle is determined by its recent volatility (standard deviation). The cycle with the highest energy is designated as the "Dominant Cycle."
Phase Analysis: The engine determines if the dominant cycles are in a bullish phase (rising from a trough) or a bearish phase (falling from a peak).
Cycle Sync: The highest conviction timing signals occur when multiple cycles (e.g., short and medium) are synchronized in the same direction, indicating broad-based momentum.
🔧 COMPREHENSIVE INPUT SYSTEM
Pillar I: Market Coherence Engine
Coherence Analysis Window (10-50, Default: 21): The lookback period for the Coherence Engine.
Lower Values (10-15): Highly responsive to rapid shifts in market control. Ideal for scalping but can be sensitive to noise.
Balanced (20-30): Excellent for day trading, capturing the ebb and flow of institutional sessions.
Higher Values (35-50): Smoother, more stable reading. Best for swing trading and identifying long-term institutional campaigns.
Coherence Activation Level (50-90%, Default: 70%): The minimum market organization required to enable signal generation.
Strict (80-90%): Only allows signals in extremely clear, powerful trends. Fewer, but potentially higher quality signals.
Standard (65-75%): A robust filter that effectively removes choppy conditions while capturing most valid institutional moves.
Lenient (50-60%): Allows signals in less-organized markets. Can be useful in ranging markets but may increase false signals.
Pillar II: Harmonic Liquidity Matrix
Institutional Liquidity Scanback (100-400, Default: 200): How far back the engine looks for institutional footprints.
Short (100-150): Focuses on recent institutional activity, providing highly relevant, immediate levels.
Long (300-400): Identifies major, long-term structural levels. These nodes are often extremely powerful but may be less frequent.
Institutional Volume Filter (1.3-3.0, Default: 1.8): The multiplier for detecting a volume spike.
High (2.5-3.0): Only registers climactic, undeniable institutional volume. Fewer, but more significant nodes.
Low (1.3-1.7): More sensitive, identifying smaller but still relevant institutional interest.
Node Clustering Distance (0.2-0.8 ATR, Default: 0.4): The ATR-based distance for merging nearby nodes.
High (0.6-0.8): Creates wider, more consolidated zones of liquidity.
Low (0.2-0.3): Creates more numerous, precise, and distinct levels.
Pillar III: Cyclical Resonance Matrix
Cycle Resonance Analysis (30-100, Default: 50): The lookback for determining cycle energy and dominance.
Short (30-40): Tunes the engine to faster, shorter-term market rhythms. Best for scalping.
Long (70-100): Aligns the timing component with the larger primary trend. Best for swing trading.
Institutional Signal Architecture
Signal Quality Mode (Professional, Elite, Supreme): Controls the strictness of the three-pillar confluence.
Professional: Loosest setting. May generate signals if two of the three pillars are in strong alignment. Increases signal frequency.
Elite: Balanced setting. Requires a clear, unambiguous resonance of all three pillars. The recommended default.
Supreme: Most stringent. Requires perfect alignment of all three pillars, with each pillar exhibiting exceptionally strong readings (e.g., coherence > 85%). The highest conviction signals.
Signal Spacing Control (5-25, Default: 10): The minimum bars between signals to prevent clutter and redundant alerts.
🎨 ADVANCED VISUAL SYSTEM
The visual architecture of Aetherium is designed not merely for aesthetics, but to provide an intuitive, at-a-glance understanding of the complex data being processed.
Harmonic Liquidity Nodes: The core visual element. Displayed as multi-layered, semi-transparent horizontal boxes.
Magnitude Visualization: The height and opacity of a node's "glow" are proportional to its volume magnitude. More significant nodes appear brighter and larger, instantly drawing the eye to key levels.
Color Coding: Standard nodes are blue/purple, while exceptionally high-magnitude nodes are highlighted in an accent color to denote critical importance.
🌌 Quantum Resonance Field: A dynamic background gradient that visualizes the overall market environment.
Color: Shifts from cool blues/purples (low coherence) to energetic greens/cyans (high coherence and organization), providing instant context.
Intensity: The brightness and opacity of the field are influenced by total market energy (a composite of coherence, momentum, and volume), making powerful market states visually apparent.
💎 Crystalline Lattice Matrix: A geometric web of lines projected from a central moving average.
Mathematical Basis: Levels are projected using multiples of the Golden Ratio (Phi ≈ 1.618) and the ATR. This visualizes the natural harmonic and fractal structure of the market. It is not arbitrary but is based on mathematical principles of market geometry.
🧠 Synaptic Flow Network: A dynamic particle system visualizing the engine's "thought process."
Node Density & Activation: The number of particles and their brightness/color are tied directly to the Market Coherence score. In high-coherence states, the network becomes a dense, bright, and organized web. In chaotic states, it becomes sparse and dim.
⚡ Institutional Energy Waves: Flowing sine waves that visualize market volatility and rhythm.
Amplitude & Speed: The height and speed of the waves are directly influenced by the ATR and volume, providing a feel for market energy.
📊 INSTITUTIONAL CONTROL MATRIX (DASHBOARD)
The dashboard is the central command console, providing a real-time, quantitative summary of each pillar's status.
Header: Displays the script title and version.
Coherence Engine Section:
State: Displays a qualitative assessment of market organization: ◉ PHASE LOCK (High Coherence), ◎ ORGANIZING (Moderate Coherence), or ○ CHAOTIC (Low Coherence). Color-coded for immediate recognition.
Power: Shows the precise Coherence percentage and a directional arrow (↗ or ↘) indicating if organization is increasing or decreasing.
Liquidity Matrix Section:
Nodes: Displays the total number of active Harmonic Liquidity Nodes currently being tracked.
Target: Shows the price level of the nearest significant Harmonic Node to the current price, representing the most immediate institutional level of interest.
Cycle Matrix Section:
Cycle: Identifies the currently dominant market cycle (e.g., "MID ") based on cycle energy.
Sync: Indicates the alignment of the cyclical forces: ▲ BULLISH , ▼ BEARISH , or ◆ DIVERGENT . This is the core timing confirmation.
Signal Status Section:
A unified status bar that provides the final verdict of the engine. It will display "QUANTUM SCAN" during neutral periods, or announce the tier and direction of an active signal (e.g., "◉ TIER 1 BUY ◉" ), highlighted with the appropriate color.
🎯 SIGNAL GENERATION LOGIC
Aetherium's signal logic is built on the principle of strict, non-negotiable confluence.
Condition 1: Context (Coherence Filter): The Market Coherence must be above the Coherence Activation Level. No signals can be generated in a chaotic market.
Condition 2: Location (Liquidity Node Interaction): Price must be actively interacting with a significant Harmonic Liquidity Node.
For a Buy Signal: Price must be rejecting the Node from below (testing it as support).
For a Sell Signal: Price must be rejecting the Node from above (testing it as resistance).
Condition 3: Timing (Cycle Alignment): The Cyclical Resonance Matrix must confirm that the dominant cycles are synchronized with the intended trade direction.
Signal Tiering: The Signal Quality Mode input determines how strictly these three conditions must be met. 'Supreme' mode, for example, might require not only that the conditions are met, but that the Market Coherence is exceptionally high and the interaction with the Node is accompanied by a significant volume spike.
Signal Spacing: A final filter ensures that signals are spaced by a minimum number of bars, preventing over-alerting in a single move.
🚀 ADVANCED TRADING STRATEGIES
The Primary Confluence Strategy: The intended use of the system. Wait for a Tier 1 (Elite/Supreme) or Tier 2 (Professional/Elite) signal to appear on the chart. This represents the alignment of all three pillars. Enter after the signal bar closes, with a stop-loss placed logically on the other side of the Harmonic Node that triggered the signal.
The Coherence Context Strategy: Use the Coherence Engine as a standalone market filter. When Coherence is high (>70%), favor trend-following strategies. When Coherence is low (<50%), avoid new directional trades or favor range-bound strategies. A sharp drop in Coherence during a trend can be an early warning of a trend's exhaustion.
Node-to-Node Trading: In a high-coherence environment, use the Harmonic Liquidity Nodes as both entry points and profit targets. For example, after a BUY signal is generated at one Node, the next Node above it becomes a logical first profit target.
⚖️ RESPONSIBLE USAGE AND LIMITATIONS
Decision Support, Not a Crystal Ball: Aetherium is an advanced decision-support tool. It is designed to identify high-probability conditions based on a model of institutional behavior. It does not predict the future.
Risk Management is Paramount: No indicator can replace a sound risk management plan. Always use appropriate position sizing and stop-losses. The signals provided are probabilistic, not certainties.
Past Performance Disclaimer: The market models used in this script are based on historical data. While robust, there is no guarantee that these patterns will persist in the future. Market conditions can and do change.
Not a "Set and Forget" System: The indicator performs best when its user understands the concepts behind the three pillars. Use the dashboard and visual cues to build a comprehensive view of the market before acting on a signal.
Backtesting is Essential: Before applying this tool to live trading, it is crucial to backtest and forward-test it on your preferred instruments and timeframes to understand its unique behavior and characteristics.
🔮 CONCLUSION
The Aetherium Institutional Market Resonance Engine represents a paradigm shift from single-variable analysis to a holistic, multi-pillar framework. By quantifying the abstract concepts of market context, location, and timing into a unified, logical system, it provides traders with an unprecedented lens into the mechanics of institutional market operations.
It is not merely an indicator, but a complete analytical engine designed to foster a deeper understanding of market dynamics. By focusing on the core principles of institutional order flow, Aetherium empowers traders to filter out market noise, identify key structural levels, and time their entries in harmony with the market's underlying rhythm.
"In all chaos there is a cosmos, in all disorder a secret order." - Carl Jung
— Dskyz, Trade with insight. Trade with confluence. Trade with Aetherium.
Z-scored ZLEMA | OquantZ-Scored ZLEMA | Oquant
This indicator combines the Zero-Lag Exponential Moving Average (ZLEMA) with Z-score normalization to present recent ZLEMA values relative to its mean. It helps users observe trend direction and momentum with reduced lag, while also highlighting potential overbought or oversold levels based on how far ZLEMA values deviate from their mean.
🧠 Concept Overview
📉 Zero Lag Exponential Moving Average (ZLEMA)
The EMA is a popular tool that calculates an average price, but unlike a simple moving average, it gives more weight to recent prices. This means the EMA reacts faster to new price changes and is less affected by older data. However, even with this weighting, the EMA still introduces some lag.
ZLEMA improves on the EMA by reducing this lag. It does this by adjusting how it accounts for previous prices, effectively "shifting" the data to better align the average with current market action. The result is an average that stays smooth but responds more quickly to real price changes—helping traders spot turning points or trend shifts earlier without being fooled by random noise.
📏 Z-score Normalization
Once ZLEMA is calculated, the indicator applies Z-score normalization to measure how far the current ZLEMA value is from its mean. The Z-score expresses this difference using standard deviations, providing a clear, standardized scale. This helps highlight when price moves are unusually strong—either upward or downward—beyond normal fluctuations.
🔍 How This Indicator Works
Smooth Price Data with ZLEMA
The indicator begins by applying the Zero-Lag Exponential Moving Average (ZLEMA) to the chosen price data. Unlike a regular moving average, ZLEMA reduces the typical delay by adjusting the input data before averaging. It does this by "shifting" the price series to remove the lag caused by older prices. This way, ZLEMA stays smooth but reacts more quickly to recent price changes—helping the indicator follow market moves faster without being too noisy.
Normalize ZLEMA values Using Z-score
Once ZLEMA is calculated, the indicator applies Z-score normalization to measure how far the current ZLEMA value is from its mean. The Z-score expresses this difference in terms of standard deviations, creating a clear, standardized scale. This helps highlight when price moves are unusually strong—either up or down—beyond normal fluctuations.
Set Signal Thresholds
Two threshold levels are set on the Z-score scale—crossing above the upper threshold is considered a long (buy) signal, indicating bullish momentum, while crossing below the lower threshold is considered a short (sell) signal, indicating bearish momentum.
Show Visual Signals on the Chart
The Z-score and bars are plotted with colors: green when Z-score is above the bullish threshold, purple when Z-score is below the bearish threshold.
⚙️ Customizable Inputs
Source: Choose the price source (close, open, etc.) for calculations.
ZLEMA Length: Adjust the ZLEMA length to control smoothness versus responsiveness.
Z-score period: Set the Z-score period to define how far back the indicator measures normal price behavior.
Thresholds: Adjust the upper and lower thresholds to control how sensitive the indicator is to strong momentum changes.
📈 Practical Use
This indicator helps identify trend directions and changes faster by combining ZLEMA with statistical analysis. It highlights when price moves are stronger than normal, making it easier to spot early signs of momentum shifts. Traders can use it to confirm trends or detect potential reversals with more timely signals.
🔔 Alert Support
This indicator includes optional built-in alert conditions that notify you when the Z-score crosses above the bullish threshold (long signal) or below the bearish threshold (short signal). You can enable these alerts to get timely updates on potential momentum shifts without constantly watching the chart.
⚠️ Disclaimer: This indicator is intended for educational and informational purposes only. Trading/investing involves risk, and past performance does not guarantee future results. Always test and evaluate indicators/strategies before applying them in live markets. Use at your own risk.
X PD&FVVisualizes the price's premium or discount relative to a moving average benchmark, highlighting mean-reversion and trend-continuation opportunities. While the underlying math is simple, the application is nuanced and can enhance decision-making in both trending and ranging market conditions.
Core Logic:
This tool calculates a custom **spread value**, defined as the distance between the current price and a chosen exponential moving average (EMA). Specifically:
When the current price is **above** the EMA, the spread is calculated as `low - EMA`.
When the price is **below** the EMA, the spread is calculated as `high - EMA`.
This approach creates a dynamic spread that reflects deviation from the EMA, with histogram bars:
Green when the spread is positive (suggesting a price premium),
Red when the spread is negative (suggesting a discount).
A secondary EMA (default 9-period) is applied to the spread itself, plotted as a smoother line over the histogram. This "EMA of spread" line can be interpreted as a moving reference level for detecting directional shifts in momentum.
Interpretation:
Zero Line = Fair Value: The horizontal zero axis represents equilibrium relative to the moving average. Movement toward or away from this line signals potential shifts in market bias.
Trend Following Use: In trending markets, traders can:
Buy when the spread dips below its EMA (discount within uptrend),
Sell when the spread rises above its EMA (premium within downtrend).
Mean Reversion Use: A return to the zero line (fair value) often acts as an **inflection point**, which traders can monitor for either:
Trend continuation (bounce away from zero), or
Reversal (cross through zero).
Customization:
EMA length (default 50) is adjustable to fit different timeframes or asset volatility.
Volatility & Momentum Nexus (VMN)Volatility & Momentum Nexus (VMN)
This indicator was designed to solve a common trader's problem: chart clutter from dozens of indicators that often contradict each other. The Volatility & Momentum Nexus ( VMN ) is not just another indicator; it's a complete analysis system that synthesizes four essential market pillars into a single, clean, and intuitive visual signal.
The goal of VMN is to identify high-probability moments where a period of accumulation (low volatility) is about to erupt into an explosive move, confirmed by trend, momentum, and volume.
VMN analyzes the real-time confluence of four critical elements:
The Trend (The Main Filter): A 100-period Exponential Moving Average (EMA) sets the overall context. The indicator will only look for buy signals above this line (in an uptrend) and sell signals below it (in a downtrend). The line's color changes for quick visualization.
Volatility (Energy Accumulation): Using Bollinger Bands Width (BBW), the indicator identifies "Squeeze" periods—when the price contracts and builds up energy. These zones are marked with a yellow background on the chart, signaling that a major move is imminent.
Momentum (The Trigger): An RSI (Relative Strength Index) acts as the trigger. A signal is only validated if momentum confirms the direction of the breakout (e.g., RSI > 55 for a buy), ensuring we enter the market with force.
Volume (The Final Confirmation): No breakout move is credible without volume. VMN checks if the volume at the time of the signal is significantly higher than its recent average, adding a vital layer of confirmation.
Green Arrow (Buy Signal): Appears ONLY when ALL the following conditions are met simultaneously:
Price is above the 100 EMA (Bullish Trend).
The chart is exiting a Squeeze zone (yellow background on the previous bar).
Price breaks above the upper Bollinger Band.
RSI is above the buy threshold (default 55).
Volume is above average.
Red Arrow (Sell Signal): Appears ONLY when all the opposite conditions are met.
Do not treat signals as blind commands to trade. They are high-probability confirmations.
Look for signals near key Support/Resistance levels for an even higher success rate.
Always set a Stop Loss (e.g., below the low of the signal candle or below the lower Bollinger Band for a buy).
All parameters (EMA, RSI, Bollinger Bands lengths, thresholds, etc.) can be customized from the settings menu to adapt the indicator to any financial asset or timeframe.
Disclaimer: This indicator is a tool for educational and analytical purposes. It does not constitute and should not be interpreted as financial advice. Trading involves significant risk. Always perform your own analysis and backtesting before risking real capital.
[blackcat] L1 Multi-Component CCIOVERVIEW
The " L1 Multi-Component CCI" is a sophisticated technical indicator designed to analyze market trends and momentum using multiple components of the Commodity Channel Index (CCI). This script calculates and combines various CCI-related metrics to provide a comprehensive view of price action, offering traders deeper insights into market dynamics. By integrating smoothed deviations, normalized ranges, and standard CCI values, this tool aims to enhance decision-making processes. It is particularly useful for identifying potential reversal points and confirming trend strength. 📈
FEATURES
Multi-Component CCI Calculation: Combines smoothed deviation, normalized range, percent above low, and standard CCI for a holistic analysis, providing a multifaceted view of market conditions.
Threshold Lines: Overbought (200), oversold (-200), bullish (100), and bearish (-100) thresholds are plotted for easy reference, helping traders quickly identify extreme market conditions.
Visual Indicators: Each component is plotted with distinct colors and line styles for clear differentiation, making it easier to interpret the data at a glance.
Customizable Alerts: The script includes commented-out buy and sell signal logic that can be enabled for automated trading notifications, allowing traders to set up alerts based on specific conditions. 🚀
Advanced Calculations: Utilizes a combination of simple moving averages (SMA) and exponential moving averages (EMA) to smooth out price data, enhancing the reliability of the indicator.
HOW TO USE
Apply the Script: Add the script to your chart on TradingView by searching for " L1 Multi-Component CCI" in the indicators section.
Observe the Plotted Lines: Pay close attention to the smoothed deviation, normalized range, percent above low, and standard CCI lines to identify potential overbought or oversold conditions.
Use Threshold Levels: Refer to the overbought, oversold, bullish, and bearish threshold lines to gauge extreme market conditions and potential reversal points.
Confirm Trends: Use the standard CCI line to confirm trend direction and momentum shifts, providing additional confirmation for your trading decisions.
Enable Alerts: If desired, uncomment the buy and sell signal logic to receive automated alerts when specific conditions are met, helping you stay informed even when not actively monitoring the chart. ⚠️
LIMITATIONS
Fixed Threshold Levels: The script uses fixed threshold levels (200, -200, 100, -100), which may need adjustment based on specific market conditions or asset volatility.
No Default Signals: The buy and sell signal logic is currently commented out, requiring manual activation if you wish to use automated alerts.
Complexity: The multi-component approach, while powerful, may be complex for novice traders to interpret, requiring a solid understanding of technical analysis concepts. 📉
Not for Isolation Use: This indicator is not designed for use in isolation; it is recommended to combine it with other tools and indicators for confirmation and a more robust analysis.
NOTES
Smoothing Techniques: The script uses a combination of simple moving averages (SMA) and exponential moving averages (EMA) for smoothing calculations, which helps in reducing noise and enhancing signal clarity.
Multi-Component Approach: The multi-component approach aims to provide a more nuanced view of market conditions compared to traditional CCI, offering a more comprehensive analysis.
Customization Potential: Traders can customize the script further by adjusting the parameters of the moving averages and other components to better suit their trading style and preferences. ✨
THANKS
Thanks to the TradingView community for their support and feedback on this script! Special thanks to those who contributed ideas and improvements, making this tool more robust and user-friendly. 🙏
Momentum + Keltner Stochastic Combo)The Momentum-Keltner-Stochastic Combination Strategy: A Technical Analysis and Empirical Validation
This study presents an advanced algorithmic trading strategy that implements a hybrid approach between momentum-based price dynamics and relative positioning within a volatility-adjusted Keltner Channel framework. The strategy utilizes an innovative "Keltner Stochastic" concept as its primary decision-making factor for market entries and exits, while implementing a dynamic capital allocation model with risk-based stop-loss mechanisms. Empirical testing demonstrates the strategy's potential for generating alpha in various market conditions through the combination of trend-following momentum principles and mean-reversion elements within defined volatility thresholds.
1. Introduction
Financial market trading increasingly relies on the integration of various technical indicators for identifying optimal trading opportunities (Lo et al., 2000). While individual indicators are often compromised by market noise, combinations of complementary approaches have shown superior performance in detecting significant market movements (Murphy, 1999; Kaufman, 2013). This research introduces a novel algorithmic strategy that synthesizes momentum principles with volatility-adjusted envelope analysis through Keltner Channels.
2. Theoretical Foundation
2.1 Momentum Component
The momentum component of the strategy builds upon the seminal work of Jegadeesh and Titman (1993), who demonstrated that stocks which performed well (poorly) over a 3 to 12-month period continue to perform well (poorly) over subsequent months. As Moskowitz et al. (2012) further established, this time-series momentum effect persists across various asset classes and time frames. The present strategy implements a short-term momentum lookback period (7 bars) to identify the prevailing price direction, consistent with findings by Chan et al. (2000) that shorter-term momentum signals can be effective in algorithmic trading systems.
2.2 Keltner Channels
Keltner Channels, as formalized by Chester Keltner (1960) and later modified by Linda Bradford Raschke, represent a volatility-based envelope system that plots bands at a specified distance from a central exponential moving average (Keltner, 1960; Raschke & Connors, 1996). Unlike traditional Bollinger Bands that use standard deviation, Keltner Channels typically employ Average True Range (ATR) to establish the bands' distance from the central line, providing a smoother volatility measure as established by Wilder (1978).
2.3 Stochastic Oscillator Principles
The strategy incorporates a modified stochastic oscillator approach, conceptually similar to Lane's Stochastic (Lane, 1984), but applied to a price's position within Keltner Channels rather than standard price ranges. This creates what we term "Keltner Stochastic," measuring the relative position of price within the volatility-adjusted channel as a percentage value.
3. Strategy Methodology
3.1 Entry and Exit Conditions
The strategy employs a contrarian approach within the channel framework:
Long Entry Condition:
Close price > Close price periods ago (momentum filter)
KeltnerStochastic < threshold (oversold within channel)
Short Entry Condition:
Close price < Close price periods ago (momentum filter)
KeltnerStochastic > threshold (overbought within channel)
Exit Conditions:
Exit long positions when KeltnerStochastic > threshold
Exit short positions when KeltnerStochastic < threshold
This methodology aligns with research by Brock et al. (1992) on the effectiveness of trading range breakouts with confirmation filters.
3.2 Risk Management
Stop-loss mechanisms are implemented using fixed price movements (1185 index points), providing definitive risk boundaries per trade. This approach is consistent with findings by Sweeney (1988) that fixed stop-loss systems can enhance risk-adjusted returns when properly calibrated.
3.3 Dynamic Position Sizing
The strategy implements an equity-based position sizing algorithm that increases or decreases contract size based on cumulative performance:
$ContractSize = \min(baseContracts + \lfloor\frac{\max(profitLoss, 0)}{equityStep}\rfloor - \lfloor\frac{|\min(profitLoss, 0)|}{equityStep}\rfloor, maxContracts)$
This adaptive approach follows modern portfolio theory principles (Markowitz, 1952) and Kelly criterion concepts (Kelly, 1956), scaling exposure proportionally to account equity.
4. Empirical Performance Analysis
Using historical data across multiple market regimes, the strategy demonstrates several key performance characteristics:
Enhanced performance during trending markets with moderate volatility
Reduced drawdowns during choppy market conditions through the dual-filter approach
Optimal performance when the threshold parameter is calibrated to market-specific characteristics (Pardo, 2008)
5. Strategy Limitations and Future Research
While effective in many market conditions, this strategy faces challenges during:
Rapid volatility expansion events where stop-loss mechanisms may be inadequate
Prolonged sideways markets with insufficient momentum
Markets with structural changes in volatility profiles
Future research should explore:
Adaptive threshold parameters based on regime detection
Integration with additional confirmatory indicators
Machine learning approaches to optimize parameter selection across different market environments (Cavalcante et al., 2016)
References
Brock, W., Lakonishok, J., & LeBaron, B. (1992). Simple technical trading rules and the stochastic properties of stock returns. The Journal of Finance, 47(5), 1731-1764.
Cavalcante, R. C., Brasileiro, R. C., Souza, V. L., Nobrega, J. P., & Oliveira, A. L. (2016). Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications, 55, 194-211.
Chan, L. K. C., Jegadeesh, N., & Lakonishok, J. (2000). Momentum strategies. The Journal of Finance, 51(5), 1681-1713.
Jegadeesh, N., & Titman, S. (1993). Returns to buying winners and selling losers: Implications for stock market efficiency. The Journal of Finance, 48(1), 65-91.
Kaufman, P. J. (2013). Trading systems and methods (5th ed.). John Wiley & Sons.
Kelly, J. L. (1956). A new interpretation of information rate. The Bell System Technical Journal, 35(4), 917-926.
Keltner, C. W. (1960). How to make money in commodities. The Keltner Statistical Service.
Lane, G. C. (1984). Lane's stochastics. Technical Analysis of Stocks & Commodities, 2(3), 87-90.
Lo, A. W., Mamaysky, H., & Wang, J. (2000). Foundations of technical analysis: Computational algorithms, statistical inference, and empirical implementation. The Journal of Finance, 55(4), 1705-1765.
Markowitz, H. (1952). Portfolio selection. The Journal of Finance, 7(1), 77-91.
Moskowitz, T. J., Ooi, Y. H., & Pedersen, L. H. (2012). Time series momentum. Journal of Financial Economics, 104(2), 228-250.
Murphy, J. J. (1999). Technical analysis of the financial markets: A comprehensive guide to trading methods and applications. New York Institute of Finance.
Pardo, R. (2008). The evaluation and optimization of trading strategies (2nd ed.). John Wiley & Sons.
Raschke, L. B., & Connors, L. A. (1996). Street smarts: High probability short-term trading strategies. M. Gordon Publishing Group.
Sweeney, R. J. (1988). Some new filter rule tests: Methods and results. Journal of Financial and Quantitative Analysis, 23(3), 285-300.
Wilder, J. W. (1978). New concepts in technical trading systems. Trend Research.
EMA and VWAP by Phil VoEMA and VWAP by Phil Vo
Description
This indicator combines two powerful technical analysis tools: Exponential Moving Averages (EMAs) and Volume Weighted Average Price (VWAP). Designed to assist traders in identifying trends and key price levels, this script overlays two customizable EMAs and a daily VWAP on your chart.
* EMA 1 (Blue): A fast-moving EMA with a default period of 9, ideal for short-term trend analysis.
* EMA 2 (Red): A slower EMA with a default period of 21, useful for confirming longer-term trends.
* VWAP (Yellow): The Volume Weighted Average Price, calculated using the typical price (HLC3) and volume, resetting daily. It serves as a dynamic support/resistance level and reflects the average price weighted by volume.
Features
* Customizable EMAs: Adjust the periods of both EMAs via the settings (minimum period: 1).
* Visual Clarity: Each line is plotted in a distinct color (Blue for EMA 1, Red for EMA 2, Yellow for VWAP) with a linewidth of 2 for easy identification.
* Daily VWAP: The VWAP resets at the start of each trading day, providing a reliable intraday reference point.
* Tooltips: Hover over the input settings to see descriptions of each EMA period.
How to Use
1. Add the indicator to your chart.
2. Customize the EMA periods in the settings if desired (defaults are 9 and 21).
3. Use the EMAs to spot trends:
* When EMA 1 crosses above EMA 2, it may signal a bullish trend.
* When EMA 1 crosses below EMA 2, it may indicate a bearish trend.
4. Use the VWAP as a dynamic support/resistance level:
* Prices above VWAP might suggest bullish momentum.
* Prices below VWAP might indicate bearish pressure.
Settings
* EMA 1 Length: Set the period for the fast EMA (default: 9).
* EMA 2 Length: Set the period for the slow EMA (default: 21).
Notes
* The VWAP resets daily by default, making it most suitable for intraday trading.
* This script is open-source under the Mozilla Public License 2.0, so feel free to study or modify it!
Author
Created by Phil Vo. Happy trading!
How to Add This to TradingView
When you publish the script:
1. Paste the description above into the "Description" field in the "Publish Script" dialog.
2. Set the title as "EMA and VWAP by Phil Vo".
3. Choose "Public" visibility and "Open" access to share it with the community.
4. Add tags like "EMA", "VWAP", "Moving Average", "Trend", and "Volume" to help users find it.
This description provides a clear explanation of the indicator’s purpose, usage instructions, and customization options, making it accessible and helpful for TradingView users. Let me know if you’d like to adjust anything!
EMA-Based Squeeze Dynamics (Gap Momentum & EWMA Projection)EMA-Based Squeeze Dynamics (Gap Momentum & EWMA Projection)
🚨 Main Utility: Early Squeeze Warning
The primary function of this indicator is to warn traders early when the market is approaching a "squeeze"—a tightening condition that often precedes significant moves or regime shifts. By visually highlighting areas of increasing tension, it helps traders anticipate potential volatility and prepare accordingly. This is intended to be a statistically and psychologically grounded replacement of so-called "fib-time-zones," which are overly-deterministic and subjective.
📌 Overview
The EMA-Based Squeeze Dynamics indicator projects future regime shifts (such as golden and death crosses) using exponential moving averages (EMAs). It employs historical interval data and current market conditions to dynamically forecast when the critical EMAs (50-period and 200-period) will reconverge, marking likely trend-change points.
This indicator leverages two core ideas:
Behavioral finance theory: Traders often collectively anticipate popular EMA crossovers, creating a self-fulfilling prophecy (normative social influence), similar to findings from Solomon Asch’s conformity experiments.
Bayesian-like updates: It utilizes historical crossover intervals as a prior, dynamically updating expectations based on evolving market data, ensuring its signals remain objectively grounded in actual market behavior.
⚙️ Technical & Mathematical Explanation
1. EMA Calculations and Regime Definitions
The indicator uses three EMAs:
Fast (9-period): Represents short-term price movement.
Medial (50-period): Indicates medium-term trend direction.
Slow (200-period): Defines long-term market sentiment.
Regime States:
Bullish: 50 EMA is above the 200 EMA.
Bearish: 50 EMA is below the 200 EMA.
A shift between these states triggers visual markers (arrows and labels) directly on the chart.
2. Gap Dynamics and Historical Intervals
At each crossover:
The indicator records the gap (distance) between the 50 and 200 EMAs.
It tracks the historical intervals between past crossovers.
An Exponentially Weighted Moving Average (EWMA) of these intervals is calculated, weighting recent intervals more heavily, dynamically updating expectations.
Important note:
After every regime shift, the projected crossover line resets its calculation. This reset is visually evident as the projection line appears to move further away after each regime change, temporarily "repelled" until the EMAs begin converging again. This ensures projections remain realistic, grounded in actual EMA convergence, and prevents overly optimistic forecasts immediately after a regime shift.
3. Gap Momentum & Adaptive Scaling
The indicator measures how quickly or slowly the gap between EMAs is changing ("gap momentum") and adjusts its forecast accordingly:
If the gap narrows rapidly, a crossover becomes more imminent.
If the gap widens, the next crossover is pushed further into the future.
The "gap factor" dynamically scales the projection based on recent gap momentum, bounded between reasonable limits (0.7–1.3).
4. Squeeze Ratio & Background Color (Visual Cues)
A "squeeze ratio" is computed when market conditions indicate tightening:
In a bullish regime, if the fast EMA is below the medial EMA (price pulling back towards long-term support), the squeeze ratio increases.
In a bearish regime, if the fast EMA rises above the medial EMA (price rallying into long-term resistance), the squeeze ratio increases.
What the Background Colors Mean:
Red Background: Indicates a bullish squeeze—price is compressing downward, hinting a bullish reversal or continuation breakout may occur soon.
Green Background: Indicates a bearish squeeze—price is compressing upward, suggesting a bearish reversal or continuation breakout could soon follow.
Opacity Explanation:
The transparency (opacity) of the background indicates the intensity of the squeeze:
High Opacity (solid color): Strong squeeze, high likelihood of imminent volatility or regime shift.
Low Opacity (faint color): Mild squeeze, signaling early stages of tightening.
Thus, more vivid colors serve as urgent visual warnings that a squeeze is rapidly intensifying.
5. Projected Next Crossover and Pseudo Crossover Mechanism
The indicator calculates an estimated future bar when a crossover (and thus, regime shift) is expected to occur. This calculation incorporates:
Historical EWMA interval.
Current squeeze intensity.
Gap momentum.
A dynamic penalty based on divergence from baseline conditions.
The "Pseudo Crossover" Explained:
A key adaptive feature is the pseudo crossover mechanism. If price action significantly deviates from the projected crossover (for example, if price stays beyond the projected line longer than expected), the indicator acknowledges the projection was incorrect and triggers a "pseudo crossover" event. Essentially, this acts as a reset, updating historical intervals with a weighted adjustment to recalibrate future predictions. In other words, if the indicator’s initial forecast proves inaccurate, it recognizes this quickly, resets itself, and tries again—ensuring it remains responsive and adaptive to actual market conditions.
🧠 Behavioral Theory: Normative Social Influence
This indicator is rooted in behavioral finance theory, specifically leveraging normative social influence (conformity). Traders commonly watch EMA signals (especially the 50 and 200 EMA crossovers). When traders collectively anticipate these signals, they begin trading ahead of actual crossovers, effectively creating self-fulfilling prophecies—similar to Solomon Asch’s famous conformity experiments, where individuals adopted group behaviors even against direct evidence.
This behavior means genuine regime shifts (actual EMA crossovers) rarely occur until EMAs visibly reconverge due to widespread anticipatory trading activity. The indicator quantifies these dynamics by objectively measuring EMA convergence and updating projections accordingly.
📊 How to Use This Indicator
Monitor the background color and opacity as primary visual cues.
A strongly colored background (solid red/green) is an early alert that a squeeze is intensifying—prepare for potential volatility or a regime shift.
Projected crossover lines give a dynamic target bar to watch for trend reversals or confirmations.
After each regime shift, expect a reset of the projection line. The line may seem initially repelled from price action, but it will recalibrate as EMAs converge again.
Trust the pseudo crossover mechanism to automatically recalibrate the indicator if its original projection misses.
🎯 Why Choose This Indicator?
Early Warning: Visual squeeze intensity helps anticipate market breakouts.
Behaviorally Grounded: Leverages real trader psychology (conformity and anticipation).
Objective & Adaptive: Uses real-time, data-driven updates rather than static levels or subjective analysis.
Easy to Interpret: Clear visual signals (arrows, labels, colors) simplify trading decisions.
Self-correcting (Pseudo Crossovers): Quickly adjusts when initial predictions miss, maintaining accuracy over time.
Summary:
The EMA-Based Squeeze Dynamics Indicator combines behavioral insights, dynamic Bayesian-like updates, intuitive visual cues, and a self-correcting pseudo crossover feature to offer traders a reliable early warning system for market squeezes and impending regime shifts. It transparently recalibrates after each regime shift and automatically resets whenever projections prove inaccurate—ensuring you always have an adaptive, realistic forecast.
Whether you're a discretionary trader or algorithmic strategist, this indicator provides a powerful tool to navigate market volatility effectively.
Happy Trading! 📈✨
Futuristic Indicator v3 - Enhanced Glow & Strength MetersTo ensure candles are display by script go to trading view settings and uncheck default Candle, Body and Wick to prevent them from plotting over your modified candles.
Futuristic Indicator v3 - Enhanced Glow & Strength Meters: Detailed Breakdown
This Modern styled Pine Script indicator is designed to enhance technical analysis by providing a visually striking OLED-style dashboard with multiple market insights. It integrates trend detection, momentum analysis, volatility tracking, and strength meters into a single, streamlined interface for traders.
1️⃣ Customizable Features for Flexibility
The indicator offers multiple user-configurable settings, allowing traders to adjust the display based on their trading strategy and preferences. Users can toggle elements such as strength meters, volatility indicators, trend arrows, moving averages, and buy/sell alerts. Additionally, background and candle colors can be customized for better readability.
🔹 Why is this useful?
Traders can customize their charts to focus on the data they care about.
Reduces chart clutter by allowing users to toggle features on or off.
2️⃣ Trend Detection Using EMAs
This indicator detects market trends using two Exponential Moving Averages (EMA):
A "Fast" EMA (shorter period) for quick trend shifts.
A "Slow" EMA (longer period) to confirm trends.
Comparison of the two EMAs determines if the trend is bullish (uptrend) or bearish (downtrend).
The indicator colors the trend lines accordingly and adds a trend arrow 📈📉 for quick visual cues.
🔹 Why is this useful?
EMA crossovers are widely used to identify trend reversals.
Provides clear visual cues for traders to confirm entry & exit points.
3️⃣ RSI-Based Momentum Analysis
The indicator integrates the Relative Strength Index (RSI) to gauge market momentum. The momentum value changes color dynamically based on whether it's in bullish (>50) or bearish (<50) territory.
🔹 Why is this useful?
RSI helps identify overbought and oversold conditions.
Detects trend strength by measuring the speed of price movements.
4️⃣ Bullish & Bearish Strength Meters
The indicator quantifies bullish and bearish market strength based on RSI and converts it into a percentage-based meter:
Bullish Strength (Long Strength)
Bearish Strength (Short Strength)
Strength meters are displayed using OLED-styled bars, dynamically changing in real-time.
🔹 Why is this useful?
Allows traders to visually gauge market sentiment at a glance.
Helps confirm if a trend has strong momentum or is losing strength.
5️⃣ Market Volatility Indicator (ATR-Based)
The indicator includes a volatility tracker using the Average True Range (ATR):
ATR is scaled up to provide easier readability.
Higher ATR values indicate higher market volatility.
🔹 Why is this useful?
Helps traders identify potential breakout or consolidation phases.
Allows better risk management by understanding price fluctuations.
6️⃣ Trend Strength Calculation
The indicator calculates trend strength based on the difference between the EMAs:
A higher trend strength value suggests a stronger directional trend.
Displayed as a percentage for better clarity.
🔹 Why is this useful?
Helps traders differentiate between strong and weak trends.
Reduces the likelihood of entering weak or choppy markets.
7️⃣ OLED-Style Dashboard for Market Data
A futuristic OLED-styled table is used to display critical market data in a visually appealing way:
Trend direction (Bullish/Bearish with an arrow 📈📉).
Current price.
Momentum value.
Strength meters (Bullish/Bearish).
Trend strength percentage.
Volatility Meter
The dashboard uses high-contrast colors and neon glow effects, making it easier to read against dark backgrounds.
🔹 Why is this useful?
Provides a centralized view of key trading metrics.
Eliminates the need to manually calculate trend strength.
8️⃣ Modern Style Neon Glow Effects
To enhance visibility, the indicator applies glowing effects to:
Moving Averages (EMAs): Highlighted with layered glow effects.
Candlesticks: Borders and wicks dynamically change color based on trend direction.
🔹 Why is this useful?
Improves readability in low-contrast or dark-mode charts.
Helps traders spot trends faster without reading numerical data.
9️⃣ Automated Buy & Sell Alerts
The script triggers alerts when momentum crosses key levels:
Above 55 → Potential Long Setup
Below 45 → Potential Short Setup.
🔹 Why is this useful?
Alerts help traders react quickly without constantly monitoring the chart.
Reduces the risk of missing critical trade opportunities.
🔹 Final Summary: Why is This Indicator Useful?
This futuristic cyberpunk-styled trading tool enhances traditional market analysis by combining technical indicators with high-visibility visuals.
🔹 Key Benefits:
✅ Customizable Display – Toggle elements based on trading needs.
✅ Trend Detection – EMAs highlight uptrends & downtrends.
✅ Momentum Tracking – RSI-based momentum gauge identifies strong moves.
✅ Strength Meters – Bullish/Bearish power is clearly visualized.
✅ Volatility Insights – ATR-based metric highlights market turbulence.
✅ Trend Strength Analysis – Quantifies trend intensity.
✅ Dashboard – Provides a centralized, easy-to-read data panel.
✅ Cyberpunk Neon Glow – Enhances clarity with stylish aesthetics.
✅ Real-Time Alerts – Helps traders react to key opportunities.
This indicator is designed to be both functional and visually appealing, making market analysis more intuitive and efficient. 🚀
Trend with ADX/EMA - Buy & Sell SignalsThis script is designed to help traders make buy and sell decisions based on trend analysis using two key methods: ADX (Average Directional Index) and EMA (Exponential Moving Averages). Here's a breakdown in simple terms:
What Does It Do?
Identifies the Trend's Strength and Direction:
Uses the ADX indicator to determine how strong the trend is.
Compares two lines (DI+ and DI−) to identify whether the trend is moving up or down.
Generates Buy and Sell Signals:
Uses two EMAs (a fast one and a slow one) to check when the price crosses key levels, signaling a possible buy or sell opportunity.
Plots visual indicators (arrows and labels) for easy interpretation.
Color-Codes the Chart:
Highlights the background in green when the trend is bullish (uptrend).
Highlights the background in red when the trend is bearish (downtrend).
Alerts the User:
Creates alerts when specific conditions for buying or selling are met.
Key Components:
1. ADX (Trend Strength & Direction)
What is ADX?
ADX measures how strong the trend is (not the direction). Higher ADX means a stronger trend.
It also calculates two lines:
DI+: Measures upward movement strength.
DI−: Measures downward movement strength.
How It Works in the Script:
If DI+ is greater than DI−, it’s a bullish trend (upward).
If DI− is greater than DI+, it’s a bearish trend (downward).
The background turns green for an uptrend and red for a downtrend.
2. EMA (Buy and Sell Decisions)
What is EMA?
EMA is a moving average that gives more weight to recent prices. It’s used to smooth out price fluctuations.
How It Works in the Script:
The script calculates two EMAs:
Fast EMA (short-term average): Reacts quickly to price changes.
Slow EMA (long-term average): Reacts slower and shows overall trends.
When the Fast EMA crosses above the Slow EMA, it’s a signal to Buy.
When the Fast EMA crosses below the Slow EMA, it’s a signal to Sell.
These signals are marked on the chart as "Buy" and "Sell" labels.
3. Buy and Sell Alerts
The script sets up alerts for the user:
Buy Alert: When a crossover indicates a bullish signal.
Sell Alert: When a crossunder indicates a bearish signal.
Visual Elements on the Chart:
Background Colors:
Green: When the DI+ line indicates an uptrend.
Red: When the DI− line indicates a downtrend.
EMA Lines:
Green Line: Fast EMA.
Red Line: Slow EMA.
Buy/Sell Labels:
"Buy" label: Shown when the Fast EMA crosses above the Slow EMA.
"Sell" label: Shown when the Fast EMA crosses below the Slow EMA.
Why Use This Script?
Trend Analysis: Helps you quickly identify the strength and direction of the market trend.
Buy/Sell Signals: Gives clear signals to enter or exit trades based on trend and EMA crossovers.
Custom Alerts: Ensures you never miss a trading opportunity by notifying you when conditions are met.
Visual Simplicity: Makes it easy to interpret trading signals with color-coded backgrounds and labeled arrows.
AuriumFlowAURIUM (GOLD-Weighted Average with Fractal Dynamics)
Aurium is a cutting-edge indicator that blends volume-weighted moving averages (VWMA), fractal geometry, and Fibonacci-inspired calculations to deliver a precise and holistic view of market trends. By dynamically adjusting to price and volume, Aurium uncovers key levels of confluence for trend reversals and continuations, making it a powerful tool for traders.
Key Features:
Dynamic Trendline (GOLD):
The central trendline is a weighted moving average based on price and volume, tuned using Fibonacci-based fast (34) and slow (144) exponential moving average lengths. This ensures the trendline adapts seamlessly to the flow of market dynamics.
Formula:
GOLD = VWMA(34) * Volume Factor + VWMA(144) * (1 - Volume Factor)
Fractal Highs and Lows:
Detects pivotal market points using a fractal lookback period (default 5, odd-numbered). Fractals identify local highs and lows over a defined window, capturing the structure of market cycles.
Trend Background Highlighting:
Bullish Zone: Price above the GOLD line with a green background.
Bearish Zone: Price below the GOLD line with a red background.
Buy and Sell Alerts:
Generates actionable signals when fractals align with GOLD. Bullish fractals confirm continuation or reversal in an uptrend, while bearish fractals validate a downtrend.
The Math Behind Aurium:
Volume-Weighted Adjustments:
By integrating volume into the calculation, Aurium dynamically emphasizes price levels with greater participation, giving traders insight into zones of institutional interest.
Formula:
VWMA = EMA(Close * Volume) / EMA(Volume)
Fractal Calculations:
Fractals are identified as local maxima (highs) or minima (lows) based on the surrounding bars, leveraging the natural symmetry in price behavior.
Fibonacci Relationships:
The 34 and 144 EMA lengths are Fibonacci numbers, offering a natural alignment with price cycles and market rhythms.
Ideal For:
Traders seeking a precise and intuitive indicator for aligning with trends and detecting reversals.
Strategies inspired by Bill Williams, with added volume and fractal-based insights.
Short-term scalpers and long-term trend-followers alike.
Unlock deeper market insights and trade with precision using Aurium!
Smoothed Source Weighted EMAThe Smoothed Source EMA is a tool designed to help traders identify potential buying and selling opportunities in the market. It combines two key elements: price smoothing (using standard deviation) and an Exponential Moving Average (EMA). The purpose is to filter out the day-to-day price fluctuations and create clearer buy and sell signals.
Key Concepts Behind the Indicator:
Price Smoothing (Standard Deviation):
To make the price action easier to follow, the indicator first "smooths" the price. This is done by looking at how much the price tends to move up and down (known as standard deviation).
It then creates two "bands" around the current price—one above and one below. These bands represent a smoothed version of the price and help filter out the noise caused by small, random price movements.
Exponential Moving Average (EMA):
The indicator also uses an Exponential Moving Average (EMA), which is a line that represents the average price over a certain period of time (but gives more weight to recent prices). The EMA helps capture the general trend of the price.
The indicator uses this EMA to compare the current price with the overall trend.
How Does the Indicator Work?
Once the indicator calculates the smoothed price bands and the EMA, it looks for specific conditions to trigger a buy or sell signal:
Long (Buy) Signal:
A buy signal happens when the smoothed price (the lower band) is above the EMA. In simple terms, the price is moving up, and the indicator is telling you it's a good time to buy.
The more "weight" or influence you give to the EMA, the slower this buy signal will appear, meaning it’ll only trigger when there’s a strong enough upward movement.
Short (Sell) Signal:
A sell signal occurs when the smoothed price (the upper band) is below the EMA. This suggests the price is moving down, and the indicator signals that it might be time to sell.
Again, the more "weight" you put on the EMA, the slower the sell signal will appear, as the indicator waits for a clearer downtrend.
Why is this Useful for Traders?
Smoothing the Price: Many traders struggle with the noise of price fluctuations, where the price moves up and down quickly without a clear trend. By smoothing the price, this indicator helps traders focus on the bigger picture and avoid reacting to every small movement.
Clear Buy and Sell Signals: The indicator generates easy-to-understand buy and sell signals based on the relationship between the smoothed price and the EMA. If the price is above the smoothed level and EMA, it’s a signal to buy. If it’s below, it’s a signal to sell.
Customizable Sensitivity: The indicator lets traders adjust how sensitive the buy and sell signals are. By changing certain settings, such as the smoothing length and the weight of the EMA, traders can make the indicator react faster or slower depending on how quickly they want to catch changes in the market.
How the Indicator Appears on the Chart:
EMA Line: A line that represents the trend of the price.
Upper and Lower Smoothed Bands: Two bands above and below the price that help identify when the price is moving up or down relative to the trend.
Buy and Sell Arrows: Small arrows on the chart show where the indicator suggests buying or selling.
Colored Bars: The bars on the chart may change color to visually indicate whether the indicator suggests a buy (green) or a sell (red).
In Summary:
The Smoothed Source EMA helps you identify trends by smoothing out price movements using standard deviation, then comparing these smoothed prices with the Exponential Moving Average (EMA).
When the smoothed price moves above or below the EMA, it gives you a signal: a buy when the smoothed price is above the EMA, and a sell when it’s below.
You can adjust how quickly or slowly these signals appear by modifying the settings, giving you control over how sensitive the indicator is to changes in the market.
This indicator is useful for traders who want to reduce noise and focus on the overall trend, using clear, visually simple signals to guide their trading decisions.
Triple CCI Strategy MFI Confirmed [Skyrexio]Overview
Triple CCI Strategy MFI Confirmed leverages 3 different periods Commodity Channel Index (CCI) indicator in conjunction Money Flow Index (MFI) and Exponential Moving Average (EMA) to obtain the high probability setups. Fast period CCI is used for having the high probability to enter in the direction of short term trend, middle and slow period CCI are used for confirmation, if market now likely in the mid and long-term uptrend. MFI is used to confirm trade with the money inflow/outflow with the high probability. EMA is used as an additional trend filter. Moreover, strategy uses exponential moving average (EMA) to trail the price when it reaches the specific level. More information in "Methodology" and "Justification of Methodology" paragraphs. The strategy opens only long trades.
Unique Features
Dynamic stop-loss system: Instead of fixed stop-loss level strategy utilizes average true range (ATR) multiplied by user given number subtracted from the position entry price as a dynamic stop loss level.
Configurable Trading Periods: Users can tailor the strategy to specific market windows, adapting to different market conditions.
Four layers trade filtering system: Strategy utilizes two different period CCI indicators, MFI and EMA indicators to confirm the signals produced by fast period CCI.
Trailing take profit level: After reaching the trailing profit activation level scrip activate the trailing of long trade using EMA. More information in methodology.
Methodology
The strategy opens long trade when the following price met the conditions:
Fast period CCI shall crossover the zero-line.
Slow and Middle period CCI shall be above zero-lines.
Price shall close above the EMA. Crossover is not obligatory
MFI shall be above 50
When long trade is executed, strategy set the stop-loss level at the price ATR multiplied by user-given value below the entry price. This level is recalculated on every next candle close, adjusting to the current market volatility.
At the same time strategy set up the trailing stop validation level. When the price crosses the level equals entry price plus ATR multiplied by user-given value script starts to trail the price with EMA. If price closes below EMA long trade is closed. When the trailing starts, script prints the label “Trailing Activated”.
Strategy settings
In the inputs window user can setup the following strategy settings:
ATR Stop Loss (by default = 1.75)
ATR Trailing Profit Activation Level (by default = 2.25)
CCI Fast Length (by default = 14, used for calculation short term period CCI)
CCI Middle Length (by default = 25, used for calculation short term period CCI)
CCI Slow Length (by default = 50, used for calculation long term period CCI)
MFI Length (by default = 14, used for calculation MFI
EMA Length (by default = 50, period of EMA, used for trend filtering EMA calculation)
Trailing EMA Length (by default = 20)
User can choose the optimal parameters during backtesting on certain price chart.
Justification of Methodology
Before understanding why this particular combination of indicator has been chosen let's briefly explain what is CCI, MFI and EMA.
The Commodity Channel Index (CCI) is a momentum-based technical indicator that measures the deviation of a security's price from its average price over a specific period. It helps traders identify overbought or oversold conditions and potential trend reversals.
The CCI formula is:
CCI = (Typical Price − SMA) / (0.015 × Mean Deviation)
Typical Price (TP): This is calculated as the average of the high, low, and closing prices for the period.
Simple Moving Average (SMA): This is the average of the Typical Prices over a specific number of periods.
Mean Deviation: This is the average of the absolute differences between the Typical Price and the SMA.
The result is a value that typically fluctuates between +100 and -100, though it is not bounded and can go higher or lower depending on the price movement.
The Money Flow Index (MFI) is a technical indicator that measures the strength of money flowing into and out of a security. It combines price and volume data to assess buying and selling pressure and is often used to identify overbought or oversold conditions. The formula for MFI involves several steps:
1. Calculate the Typical Price (TP):
TP = (high + low + close) / 3
2. Calculate the Raw Money Flow (RMF):
Raw Money Flow = TP × Volume
3. Determine Positive and Negative Money Flow:
If the current TP is greater than the previous TP, it's Positive Money Flow.
If the current TP is less than the previous TP, it's Negative Money Flow.
4. Calculate the Money Flow Ratio (MFR):
Money Flow Ratio = Sum of Positive Money Flow (over n periods) / Sum of Negative Money Flow (over n periods)
5. Calculate the Money Flow Index (MFI):
MFI = 100 − (100 / (1 + Money Flow Ratio))
MFI above 80 can be considered as overbought, below 20 - oversold.
The Exponential Moving Average (EMA) is a type of moving average that places greater weight and significance on the most recent data points. It is widely used in technical analysis to smooth price data and identify trends more quickly than the Simple Moving Average (SMA).
Formula:
1. Calculate the multiplier
Multiplier = 2 / (n + 1) , Where n is the number of periods.
2. EMA Calculation
EMA = (Current Price) × Multiplier + (Previous EMA) × (1 − Multiplier)
This strategy leverages Fast period CCI, which shall break the zero line to the upside to say that probability of short term trend change to the upside increased. This zero line crossover shall be confirmed by the Middle and Slow periods CCI Indicators. At the moment of breakout these two CCIs shall be above 0, indicating that there is a high probability that price is in middle and long term uptrend. This approach increases chances to have a long trade setup in the direction of mid-term and long-term trends when the short-term trend starts to reverse to the upside.
Additionally strategy uses MFI to have a greater probability that fast CCI breakout is confirmed by this indicator. We consider the values of MFI above 50 as a higher probability that trend change from downtrend to the uptrend is real. Script opens long trades only if MFI is above 50. As you already know from the MFI description, it incorporates volume in its calculation, therefore we have another one confirmation factor.
Finally, strategy uses EMA an additional trend filter. It allows to open long trades only if price close above EMA (by default 50 period). It increases the probability of taking long trades only in the direction of the trend.
ATR is used to adjust the strategy risk management to the current market volatility. If volatility is low, we don’t need the large stop loss to understand the there is a high probability that we made a mistake opening the trade. User can setup the settings ATR Stop Loss and ATR Trailing Profit Activation Level to realize his own risk to reward preferences, but the unique feature of a strategy is that after reaching trailing profit activation level strategy is trying to follow the trend until it is likely to be finished instead of using fixed risk management settings. It allows sometimes to be involved in the large movements. It’s also important to make a note, that script uses another one EMA (by default = 20 period) as a trailing profit level.
Backtest Results
Operating window: Date range of backtests is 2022.04.01 - 2024.11.25. It is chosen to let the strategy to close all opened positions.
Commission and Slippage: Includes a standard Binance commission of 0.1% and accounts for possible slippage over 5 ticks.
Initial capital: 10000 USDT
Percent of capital used in every trade: 50%
Maximum Single Position Loss: -4.13%
Maximum Single Profit: +19.66%
Net Profit: +5421.21 USDT (+54.21%)
Total Trades: 108 (44.44% win rate)
Profit Factor: 2.006
Maximum Accumulated Loss: 777.40 USDT (-7.77%)
Average Profit per Trade: 50.20 USDT (+0.85%)
Average Trade Duration: 44 hours
These results are obtained with realistic parameters representing trading conditions observed at major exchanges such as Binance and with realistic trading portfolio usage parameters.
How to Use
Add the script to favorites for easy access.
Apply to the desired timeframe and chart (optimal performance observed on 2h BTC/USDT).
Configure settings using the dropdown choice list in the built-in menu.
Set up alerts to automate strategy positions through web hook with the text: {{strategy.order.alert_message}}
Disclaimer:
Educational and informational tool reflecting Skyrex commitment to informed trading. Past performance does not guarantee future results. Test strategies in a simulated environment before live implementation
Eze Profit - VWAP + MACD Combined SignalThe Eze Profit - VWAP + MACD Combined Signal is an advanced trading tool designed to help traders align price trends with momentum confirmation for better decision-making. By combining Volume-Weighted Average Price (VWAP) and Moving Average Convergence Divergence (MACD), this indicator provides clear entry and exit signals, allowing traders to follow trends and take advantage of momentum shifts.
How It Works:
VWAP:
VWAP represents the average price of an asset, weighted by volume, over a specific period.
It acts as a dynamic support/resistance level and trend filter. Price above VWAP indicates bullish conditions, while price below VWAP suggests bearish conditions.
MACD:
MACD measures momentum through the difference between fast and slow exponential moving averages (EMAs).
Signals are generated when the MACD line crosses its signal line:
Bullish Crossover: Indicates increasing upward momentum.
Bearish Crossunder: Indicates increasing downward momentum.
Combined Logic:
Long Signal: Triggered when price is above VWAP, and MACD exhibits a bullish crossover.
Short Signal: Triggered when price is below VWAP, and MACD exhibits a bearish crossunder.
The script tracks the trader's "in-position" state to prevent redundant signals and ensure clarity.
How to Use:
Use this script to identify potential long and short trading opportunities:
Buy Signal: Enter a long position when the price moves above VWAP and MACD confirms bullish momentum.
Sell Signal: Exit or short when the price drops below VWAP and MACD confirms bearish momentum.
Combine with additional tools like support/resistance, volume analysis, or candlestick patterns for confirmation.
Features:
VWAP Trend Filter: Dynamically adjusts to the trading session to identify overall trend direction.
MACD Momentum Confirmation: Detects key momentum shifts with configurable settings for fast, slow, and signal lengths.
Position State Tracking: Avoids signal redundancy by monitoring open positions.
Buy/Sell Visualizations: Plots Buy/Sell signals directly on the chart for ease of use.
Alerts: Notifies traders in real-time when a long or short signal is triggered.
Customizable Settings:
MACD Fast Length, Slow Length, and Signal Smoothing parameters.
VWAP timeframe resolution to adapt to different trading styles (e.g., intraday or daily).
Credits:
This script is based on standard VWAP and MACD calculations provided by TradingView’s library and has been enhanced with unique logic for combined signal generation.
Notes:
This indicator is intended for educational purposes and should not be considered financial advice. Use it as part of a broader trading strategy alongside other tools for optimal results.
followerFollower Indicator
This custom Follower Indicator is designed to track market trends and generate buy/sell signals based on price movements and adaptive moving averages. The indicator adjusts dynamically to market conditions using an Exponential Moving Average (EMA) and a smoothed average of the high-low range over the last 20 bars.
Key Features:
Adaptive Trend Following: The indicator uses an EMA of the close price along with a dynamically adjusted range (high-low) to create an adaptive trend-following line.
Buy and Sell Signals: Buy signals are generated when the EMA crosses above the follower line, while sell signals occur when the follower line crosses above the EMA.
Dynamic Color Coding: The indicator line changes color based on the relationship between the price and the follower line. It turns blue when the price is above the follower line and red when the price is below.
Customizable Parameters: Users can adjust the range multiplier (oran) and the EMA period (uzunluk) to fine-tune the indicator to different market conditions.
How to Use:
Buy Signal: A buy signal is triggered when the EMA crosses above the follower line.
Sell Signal: A sell signal is triggered when the follower line crosses above the EMA.
Notes:
This indicator is intended to help identify market trends and potential entry/exit points based on price behavior and momentum.
It is recommended to use this indicator in conjunction with other technical analysis tools and risk management strategies.
Feel free to adjust the parameters based on your trading style and preferences. Happy trading!
