ZRK 30m This TradingView indicator draws alternating 30-minute boxes aligned precisely to real clock times (e.g., 10:00, 10:30, 11:00), helping traders visually segment intraday price action. It highlights every other 30-minute block with customizable colors, line styles, and opacity, allowing users to clearly differentiate between trading intervals. The boxes automatically adjust based on the chart’s timeframe, maintaining accuracy on 1-minute to 60-minute charts. Optional time labels can also be displayed for additional context. This tool is useful for identifying patterns, measuring volatility, or applying breakout strategies based on defined, consistent time windows across global trading sessions.
在腳本中搜尋"腾讯10大股东"
Enhanced Fuzzy SMA Analyzer (Multi-Output Proxy) [FibonacciFlux]EFzSMA: Decode Trend Quality, Conviction & Risk Beyond Simple Averages
Stop Relying on Lagging Averages Alone. Gain a Multi-Dimensional Edge.
The Challenge: Simple Moving Averages (SMAs) tell you where the price was , but they fail to capture the true quality, conviction, and sustainability of a trend. Relying solely on price crossing an average often leads to chasing weak moves, getting caught in choppy markets, or missing critical signs of trend exhaustion. Advanced traders need a more sophisticated lens to navigate complex market dynamics.
The Solution: Enhanced Fuzzy SMA Analyzer (EFzSMA)
EFzSMA is engineered to address these limitations head-on. It moves beyond simple price-average comparisons by employing a sophisticated Fuzzy Inference System (FIS) that intelligently integrates multiple critical market factors:
Price deviation from the SMA ( adaptively normalized for market volatility)
Momentum (Rate of Change - ROC)
Market Sentiment/Overheat (Relative Strength Index - RSI)
Market Volatility Context (Average True Range - ATR, optional)
Volume Dynamics (Volume relative to its MA, optional)
Instead of just a line on a chart, EFzSMA delivers a multi-dimensional assessment designed to give you deeper insights and a quantifiable edge.
Why EFzSMA? Gain Deeper Market Insights
EFzSMA empowers you to make more informed decisions by providing insights that simple averages cannot:
Assess True Trend Quality, Not Just Location: Is the price above the SMA simply because of a temporary spike, or is it supported by strong momentum, confirming volume, and stable volatility? EFzSMA's core fuzzyTrendScore (-1 to +1) evaluates the health of the trend, helping you distinguish robust moves from noise.
Quantify Signal Conviction: How reliable is the current trend signal? The Conviction Proxy (0 to 1) measures the internal consistency among the different market factors analyzed by the FIS. High conviction suggests factors are aligned, boosting confidence in the trend signal. Low conviction warns of conflicting signals, uncertainty, or potential consolidation – acting as a powerful filter against chasing weak moves.
// Simplified Concept: Conviction reflects agreement vs. conflict among fuzzy inputs
bullStrength = strength_SB + strength_WB
bearStrength = strength_SBe + strength_WBe
dominantStrength = max(bullStrength, bearStrength)
conflictingStrength = min(bullStrength, bearStrength) + strength_N
convictionProxy := (dominantStrength - conflictingStrength) / (dominantStrength + conflictingStrength + 1e-10)
// Modifiers (Volatility/Volume) applied...
Anticipate Potential Reversals: Trends don't last forever. The Reversal Risk Proxy (0 to 1) synthesizes multiple warning signs – like extreme RSI readings, surging volatility, or diverging volume – into a single, actionable metric. High reversal risk flags conditions often associated with trend exhaustion, providing early warnings to protect profits or consider counter-trend opportunities.
Adapt to Changing Market Regimes: Markets shift between high and low volatility. EFzSMA's unique Adaptive Deviation Normalization adjusts how it perceives price deviations based on recent market behavior (percentile rank). This ensures more consistent analysis whether the market is quiet or chaotic.
// Core Idea: Normalize deviation by recent volatility (percentile)
diff_abs_percentile = ta.percentile_linear_interpolation(abs(raw_diff), normLookback, percRank) + 1e-10
normalized_diff := raw_diff / diff_abs_percentile
// Fuzzy sets for 'normalized_diff' are thus adaptive to volatility
Integrate Complexity, Output Clarity: EFzSMA distills complex, multi-factor analysis into clear, interpretable outputs, helping you cut through market noise and focus on what truly matters for your decision-making process.
Interpreting the Multi-Dimensional Output
The true power of EFzSMA lies in analyzing its outputs together:
A high Trend Score (+0.8) is significant, but its reliability is amplified by high Conviction (0.9) and low Reversal Risk (0.2) . This indicates a strong, well-supported trend.
Conversely, the same high Trend Score (+0.8) coupled with low Conviction (0.3) and high Reversal Risk (0.7) signals caution – the trend might look strong superficially, but internal factors suggest weakness or impending exhaustion.
Use these combined insights to:
Filter Entry Signals: Require minimum Trend Score and Conviction levels.
Manage Risk: Consider reducing exposure or tightening stops when Reversal Risk climbs significantly, especially if Conviction drops.
Time Exits: Use rising Reversal Risk and falling Conviction as potential signals to take profits.
Identify Regime Shifts: Monitor how the relationship between the outputs changes over time.
Core Technology (Briefly)
EFzSMA leverages a Mamdani-style Fuzzy Inference System. Crisp inputs (normalized deviation, ROC, RSI, ATR%, Vol Ratio) are mapped to linguistic fuzzy sets ("Low", "High", "Positive", etc.). A rules engine evaluates combinations (e.g., "IF Deviation is LargePositive AND Momentum is StrongPositive THEN Trend is StrongBullish"). Modifiers based on Volatility and Volume context adjust rule strengths. Finally, the system aggregates these and defuzzifies them into the Trend Score, Conviction Proxy, and Reversal Risk Proxy. The key is the system's ability to handle ambiguity and combine multiple, potentially conflicting factors in a nuanced way, much like human expert reasoning.
Customization
While designed with robust defaults, EFzSMA offers granular control:
Adjust SMA, ROC, RSI, ATR, Volume MA lengths.
Fine-tune Normalization parameters (lookback, percentile). Note: Fuzzy set definitions for deviation are tuned for the normalized range.
Configure Volatility and Volume thresholds for fuzzy sets. Tuning these is crucial for specific assets/timeframes.
Toggle visual elements (Proxies, BG Color, Risk Shapes, Volatility-based Transparency).
Recommended Use & Caveats
EFzSMA is a sophisticated analytical tool, not a standalone "buy/sell" signal generator.
Use it to complement your existing strategy and analysis.
Always validate signals with price action, market structure, and other confirming factors.
Thorough backtesting and forward testing are essential to understand its behavior and tune parameters for your specific instruments and timeframes.
Fuzzy logic parameters (membership functions, rules) are based on general heuristics and may require optimization for specific market niches.
Disclaimer
Trading involves substantial risk. EFzSMA is provided for informational and analytical purposes only and does not constitute financial advice. No guarantee of profit is made or implied. Past performance is not indicative of future results. Use rigorous risk management practices.
Fuzzy SMA Trend Analyzer (experimental)[FibonacciFlux]Fuzzy SMA Trend Analyzer (Normalized): Advanced Market Trend Detection Using Fuzzy Logic Theory
Elevate your technical analysis with institutional-grade fuzzy logic implementation
Research Genesis & Conceptual Framework
This indicator represents the culmination of extensive research into applying fuzzy logic theory to financial markets. While traditional technical indicators often produce binary outcomes, market conditions exist on a continuous spectrum. The Fuzzy SMA Trend Analyzer addresses this limitation by implementing a sophisticated fuzzy logic system that captures the nuanced, multi-dimensional nature of market trends.
Core Fuzzy Logic Principles
At the heart of this indicator lies fuzzy logic theory - a mathematical framework designed to handle imprecision and uncertainty:
// Improved fuzzy_triangle function with guard clauses for NA and invalid parameters.
fuzzy_triangle(val, left, center, right) =>
if na(val) or na(left) or na(center) or na(right) or left > center or center > right // Guard checks
0.0
else if left == center and center == right // Crisp set (single point)
val == center ? 1.0 : 0.0
else if left == center // Left-shoulder shape (ramp down from 1 at center to 0 at right)
val >= right ? 0.0 : val <= center ? 1.0 : (right - val) / (right - center)
else if center == right // Right-shoulder shape (ramp up from 0 at left to 1 at center)
val <= left ? 0.0 : val >= center ? 1.0 : (val - left) / (center - left)
else // Standard triangle
math.max(0.0, math.min((val - left) / (center - left), (right - val) / (right - center)))
This implementation of triangular membership functions enables the indicator to transform crisp numerical values into degrees of membership in linguistic variables like "Large Positive" or "Small Negative," creating a more nuanced representation of market conditions.
Dynamic Percentile Normalization
A critical innovation in this indicator is the implementation of percentile-based normalization for SMA deviation:
// ----- Deviation Scale Estimation using Percentile -----
// Calculate the percentile rank of the *absolute* deviation over the lookback period.
// This gives an estimate of the 'typical maximum' deviation magnitude recently.
diff_abs_percentile = ta.percentile_linear_interpolation(math.abs(raw_diff), normLookback, percRank) + 1e-10
// ----- Normalize the Raw Deviation -----
// Divide the raw deviation by the estimated 'typical max' magnitude.
normalized_diff = raw_diff / diff_abs_percentile
// ----- Clamp the Normalized Deviation -----
normalized_diff_clamped = math.max(-3.0, math.min(3.0, normalized_diff))
This percentile normalization approach creates a self-adapting system that automatically calibrates to different assets and market regimes. Rather than using fixed thresholds, the indicator dynamically adjusts based on recent volatility patterns, significantly enhancing signal quality across diverse market environments.
Multi-Factor Fuzzy Rule System
The indicator implements a comprehensive fuzzy rule system that evaluates multiple technical factors:
SMA Deviation (Normalized): Measures price displacement from the Simple Moving Average
Rate of Change (ROC): Captures price momentum over a specified period
Relative Strength Index (RSI): Assesses overbought/oversold conditions
These factors are processed through a sophisticated fuzzy inference system with linguistic variables:
// ----- 3.1 Fuzzy Sets for Normalized Deviation -----
diffN_LP := fuzzy_triangle(normalized_diff_clamped, 0.7, 1.5, 3.0) // Large Positive (around/above percentile)
diffN_SP := fuzzy_triangle(normalized_diff_clamped, 0.1, 0.5, 0.9) // Small Positive
diffN_NZ := fuzzy_triangle(normalized_diff_clamped, -0.2, 0.0, 0.2) // Near Zero
diffN_SN := fuzzy_triangle(normalized_diff_clamped, -0.9, -0.5, -0.1) // Small Negative
diffN_LN := fuzzy_triangle(normalized_diff_clamped, -3.0, -1.5, -0.7) // Large Negative (around/below percentile)
// ----- 3.2 Fuzzy Sets for ROC -----
roc_HN := fuzzy_triangle(roc_val, -8.0, -5.0, -2.0)
roc_WN := fuzzy_triangle(roc_val, -3.0, -1.0, -0.1)
roc_NZ := fuzzy_triangle(roc_val, -0.3, 0.0, 0.3)
roc_WP := fuzzy_triangle(roc_val, 0.1, 1.0, 3.0)
roc_HP := fuzzy_triangle(roc_val, 2.0, 5.0, 8.0)
// ----- 3.3 Fuzzy Sets for RSI -----
rsi_L := fuzzy_triangle(rsi_val, 0.0, 25.0, 40.0)
rsi_M := fuzzy_triangle(rsi_val, 35.0, 50.0, 65.0)
rsi_H := fuzzy_triangle(rsi_val, 60.0, 75.0, 100.0)
Advanced Fuzzy Inference Rules
The indicator employs a comprehensive set of fuzzy rules that encode expert knowledge about market behavior:
// --- Fuzzy Rules using Normalized Deviation (diffN_*) ---
cond1 = math.min(diffN_LP, roc_HP, math.max(rsi_M, rsi_H)) // Strong Bullish: Large pos dev, strong pos roc, rsi ok
strength_SB := math.max(strength_SB, cond1)
cond2 = math.min(diffN_SP, roc_WP, rsi_M) // Weak Bullish: Small pos dev, weak pos roc, rsi mid
strength_WB := math.max(strength_WB, cond2)
cond3 = math.min(diffN_SP, roc_NZ, rsi_H) // Weakening Bullish: Small pos dev, flat roc, rsi high
strength_N := math.max(strength_N, cond3 * 0.6) // More neutral
strength_WB := math.max(strength_WB, cond3 * 0.2) // Less weak bullish
This rule system evaluates multiple conditions simultaneously, weighting them by their degree of membership to produce a comprehensive trend assessment. The rules are designed to identify various market conditions including strong trends, weakening trends, potential reversals, and neutral consolidations.
Defuzzification Process
The final step transforms the fuzzy result back into a crisp numerical value representing the overall trend strength:
// --- Step 6: Defuzzification ---
denominator = strength_SB + strength_WB + strength_N + strength_WBe + strength_SBe
if denominator > 1e-10 // Use small epsilon instead of != 0.0 for float comparison
fuzzyTrendScore := (strength_SB * STRONG_BULL +
strength_WB * WEAK_BULL +
strength_N * NEUTRAL +
strength_WBe * WEAK_BEAR +
strength_SBe * STRONG_BEAR) / denominator
The resulting FuzzyTrendScore ranges from -1 (strong bearish) to +1 (strong bullish), providing a smooth, continuous evaluation of market conditions that avoids the abrupt signal changes common in traditional indicators.
Advanced Visualization with Rainbow Gradient
The indicator incorporates sophisticated visualization using a rainbow gradient coloring system:
// Normalize score to for gradient function
normalizedScore = na(fuzzyTrendScore) ? 0.5 : math.max(0.0, math.min(1.0, (fuzzyTrendScore + 1) / 2))
// Get the color based on gradient setting and normalized score
final_color = get_gradient(normalizedScore, gradient_type)
This color-coding system provides intuitive visual feedback, with color intensity reflecting trend strength and direction. The gradient can be customized between Red-to-Green or Red-to-Blue configurations based on user preference.
Practical Applications
The Fuzzy SMA Trend Analyzer excels in several key applications:
Trend Identification: Precisely identifies market trend direction and strength with nuanced gradation
Market Regime Detection: Distinguishes between trending markets and consolidation phases
Divergence Analysis: Highlights potential reversals when price action and fuzzy trend score diverge
Filter for Trading Systems: Provides high-quality trend filtering for other trading strategies
Risk Management: Offers early warning of potential trend weakening or reversal
Parameter Customization
The indicator offers extensive customization options:
SMA Length: Adjusts the baseline moving average period
ROC Length: Controls momentum sensitivity
RSI Length: Configures overbought/oversold sensitivity
Normalization Lookback: Determines the adaptive calculation window for percentile normalization
Percentile Rank: Sets the statistical threshold for deviation normalization
Gradient Type: Selects the preferred color scheme for visualization
These parameters enable fine-tuning to specific market conditions, trading styles, and timeframes.
Acknowledgments
The rainbow gradient visualization component draws inspiration from LuxAlgo's "Rainbow Adaptive RSI" (used under CC BY-NC-SA 4.0 license). This implementation of fuzzy logic in technical analysis builds upon Fermi estimation principles to overcome the inherent limitations of crisp binary indicators.
This indicator is shared under Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license.
Remember that past performance does not guarantee future results. Always conduct thorough testing before implementing any technical indicator in live trading.
Adaptive Regression Channel [MissouriTim]The Adaptive Regression Channel (ARC) is a technical indicator designed to empower traders with a clear, adaptable, and precise view of market trends and price boundaries. By blending advanced statistical techniques with real-time market data, ARC delivers a comprehensive tool that dynamically adjusts to price action, volatility, volume, and momentum. Whether you’re navigating the fast-paced world of cryptocurrencies, the steady trends of stocks, or the intricate movements of FOREX pairs, ARC provides a robust framework for identifying opportunities and managing risk.
Core Components
1. Color-Coded Regression Line
ARC’s centerpiece is a linear regression line derived from a Weighted Moving Average (WMA) of closing prices. This line adapts its calculation period based on market volatility (via ATR) and is capped between a minimum of 20 bars and a maximum of 1.5 times the user-defined base length (default 100). Visually, it shifts colors to reflect trend direction: green for an upward slope (bullish) and red for a downward slope (bearish), offering an instant snapshot of market sentiment.
2. Dynamic Residual Channels
Surrounding the regression line are upper (red) and lower (green) channels, calculated using the standard deviation of residuals—the difference between actual closing prices and the regression line. This approach ensures the channels precisely track how closely prices follow the trend, rather than relying solely on overall price volatility. The channel width is dynamically adjusted by a multiplier that factors in:
Volatility: Measured through the Average True Range (ATR), widening channels during turbulent markets.
Trend Strength: Based on the regression slope, expanding channels in strong trends and contracting them in consolidation phases.
3. Volume-Weighted Moving Average (VWMA)
Plotted in orange, the VWMA overlays a volume-weighted price trend, emphasizing movements backed by significant trading activity. This complements the regression line, providing additional confirmation of trend validity and potential breakout strength.
4. Scaled RSI Overlay
ARC features a Relative Strength Index (RSI) overlay, plotted in purple and scaled to hover closely around the regression line. This compact display reflects momentum shifts within the trend’s context, keeping RSI visible on the price chart without excessive swings. User-defined overbought (default 70) and oversold (default 30) levels offer reference points for momentum analysis."
Technical Highlights
ARC leverages a volatility-adjusted lookback period, residual-based channel construction, and multi-indicator integration to achieve high accuracy. Its parameters—such as base length, channel width, ATR period, and RSI length—are fully customizable, allowing traders to tailor it to their specific needs.
Why Choose ARC?
ARC stands out for its adaptability and precision. The residual-based channels offer tighter, more relevant support and resistance levels compared to standard volatility measures, while the dynamic adjustments ensure it performs well in both trending and ranging markets. The inclusion of VWMA and scaled RSI adds depth, merging trend, volume, and momentum into a single, cohesive overlay. For traders seeking a versatile, all-in-one indicator, ARC delivers actionable insights with minimal noise.
Best Ways to Use the Adaptive Regression Channel (ARC)
The Adaptive Regression Channel (ARC) is a flexible tool that supports a variety of trading strategies, from trend-following to breakout detection. Below are the most effective ways to use ARC, along with practical tips for maximizing its potential. Adjustments to its settings may be necessary depending on the timeframe (e.g., intraday vs. daily) and the asset being traded (e.g., stocks, FOREX, cryptocurrencies), as each market exhibits unique volatility and behavior.
1. Trend Following
• How to Use: Rely on the regression line’s color to guide your trades. A green line (upward slope) signals a bullish trend—consider entering or holding long positions. A red line (downward slope) indicates a bearish trend—look to short or exit longs.
• Best Practice: Confirm the trend with the VWMA (orange line). Price above the VWMA in a green uptrend strengthens the bullish case; price below in a red downtrend reinforces bearish momentum.
• Adjustment: For short timeframes like 15-minute crypto charts, lower the Base Regression Length (e.g., to 50) for quicker trend detection. For weekly stock charts, increase it (e.g., to 200) to capture broader movements.
2. Channel-Based Trades
• How to Use: Use the upper channel (red) as resistance and the lower channel (green) as support. Buy when the price bounces off the lower channel in an uptrend, and sell or short when it rejects the upper channel in a downtrend.
• Best Practice: Check the scaled RSI (purple line) for momentum cues. A low RSI (e.g., near 30) at the lower channel suggests a stronger buy signal; a high RSI (e.g., near 70) at the upper channel supports a sell.
• Adjustment: In volatile crypto markets, widen the Base Channel Width Coefficient (e.g., to 2.5) to reduce false signals. For stable FOREX pairs (e.g., EUR/USD), a narrower width (e.g., 1.5) may work better.
3. Breakout Detection
• How to Use: Watch for price breaking above the upper channel (bullish breakout) or below the lower channel (bearish breakout). These moves often signal strong momentum shifts.
• Best Practice: Validate breakouts with VWMA position—price above VWMA for bullish breaks, below for bearish—and ensure the regression line’s slope aligns (green for up, red for down).
• Adjustment: For fast-moving assets like crypto on 1-hour charts, shorten ATR Length (e.g., to 7) to make channels more reactive. For stocks on daily charts, keep it at 14 or higher for reliability.
4. Momentum Analysis
• How to Use: The scaled RSI overlay shows momentum relative to the regression line. Rising RSI in a green uptrend confirms bullish strength; falling RSI in a red downtrend supports bearish pressure.
• Best Practice: Look for RSI divergences—e.g., price hitting new highs at the upper channel while RSI flattens or drops could signal an impending reversal.
• Adjustment: Reduce RSI Length (e.g., to 7) for intraday trading in FOREX or crypto to catch short-term momentum shifts. Increase it (e.g., to 21) for longer-term stock trades.
5. Range Trading
• How to Use: When the regression line’s slope is near zero (flat) and channels are tight, ARC indicates a ranging market. Buy near the lower channel and sell near the upper channel, targeting the regression line as the mean price.
• Best Practice: Ensure VWMA hovers close to the regression line to confirm the range-bound state.
• Adjustment: For low-volatility stocks on daily charts, use a moderate Base Regression Length (e.g., 100) and tight Base Channel Width (e.g., 1.5). For choppy crypto markets, test shorter settings.
Optimization Strategies
• Timeframe Customization: Adjust ARC’s parameters to match your trading horizon. Short timeframes (e.g., 1-minute to 1-hour) benefit from lower Base Regression Length (20–50) and ATR Length (7–10) for agility, while longer timeframes (e.g., daily, weekly) favor higher values (100–200 and 14–21) for stability.
• Asset-Specific Tuning:
○ Stocks: Use longer lengths (e.g., 100–200) and moderate widths (e.g., 1.8) for stable equities; tweak ATR Length based on sector volatility (shorter for tech, longer for utilities).
○ FOREX: Set Base Regression Length to 50–100 and Base Channel Width to 1.5–2.0 for smoother trends; adjust RSI Length (e.g., 10–14) based on pair volatility.
○ Crypto: Opt for shorter lengths (e.g., 20–50) and wider widths (e.g., 2.0–3.0) to handle rapid price swings; use a shorter ATR Length (e.g., 7) for quick adaptation.
• Backtesting: Test ARC on historical data for your asset and timeframe to optimize settings. Evaluate how often price respects channels and whether breakouts yield profitable trades.
• Enhancements: Pair ARC with volume surges, key support/resistance levels, or candlestick patterns (e.g., doji at channel edges) for higher-probability setups.
Practical Considerations
ARC’s adaptability makes it suitable for diverse markets, but its performance hinges on proper calibration. Cryptocurrencies, with their high volatility, may require shorter, wider settings to capture rapid moves, while stocks on longer timeframes benefit from broader, smoother configurations. FOREX pairs often fall in between, depending on their inherent volatility. Experiment with the adjustable parameters to align ARC with your trading style and market conditions, ensuring it delivers the precision and reliability you need.
Deviation ChannelsIndicator Name: Deviation Channels (Dev Chan)
Why Use This Indicator?
Visualize Volatility Ranges:
The indicator plots Keltner Channels at four levels above and below an average line, letting you easily see how far price has deviated from a typical range. Each “dev” line highlights potential support or resistance during pullbacks or surges.
Color-Coded Clarity:
Each band shifts color intensity depending on whether the current price is trading above or below it, letting you spot breakouts and rejections at a glance. Meanwhile, the Fast SMA (default 10) also changes color – green if price is above, red if below – adding a quick momentum read.
Adjustable Source & Length:
Choose your input source (open, close, ohlc4, or hlc3) and set your Keltner length to suit different asset classes or timeframes. Whether you want a tighter, more reactive channel or a smoother, longer-term reading, the script adapts with minimal effort.
A Simple Trading Approach
Identify Trend with Fast SMA:
If the Fast SMA (default length 10) is green (price above it), treat that as a bullish environment. If it’s red (price below), favor bearish or neutral stances.
Wait for Price to Reach Lower/Upper Deviations:
In a bullish setup (Fast SMA green), watch for price to dip into one of the lower channels (e.g., -1 Dev or -2 Dev). Such pullbacks can become potential “buy the dip” zones if price stabilizes and resumes upward momentum.
Conversely, if the Fast SMA is red, watch for price to test the upper channels (1 Dev or 2 Dev). That might be a short opportunity or a place to close out any remaining longs before a deeper correction.
Manage Risk with Channel Levels:
Place stop-losses just beyond the next “dev” band to protect against volatility. For example, if you enter on a bounce at -1 Dev, consider placing a stop near -2 Dev or -3 Dev, depending on your risk tolerance.
Take Profits Gradually:
In an uptrend, you might scale out of positions as price moves toward higher lines (e.g., 1 Dev or 2 Dev). Conversely, if price fails to hold above the Fast SMA or repeatedly closes below a key band, it might be time to exit.
Disclaimer: No single indicator is foolproof. Always combine with sound risk management, observe multiple timeframes, and consider fundamental factors before making trading decisions. Experiment with the Keltner length and Fast SMA fastLength to find the sweet spot for your market and time horizon.
EM Yield Curve IndexThis script calculates the Emerging Markets (EM) Yield Curve Index by aggregating the 2-year and 10-year bond yields of major emerging economies. The bond yields are weighted based on each country's bond market size, with data sourced from TradingView. The yield curve is derived by subtracting the 2-year yield from the 10-year yield, providing insights into economic conditions, risk sentiment, and potential recessions in emerging markets. The resulting EM Yield Curve Index is plotted for visualization.
Note: In some cases, TradingView's TVC data did not provide a 2-year bond yield. When this occurred, the best available alternative yield (such as 3-month, 1-year or 4-year yields) was used to approximate the short-term interest rate for that country.
Momentum Volume Divergence (MVD) EnhancedMomentum Volume Divergence (MVD) Enhanced is a powerful indicator that detects price-momentum divergences and momentum suppression for reversal trading. Optimized for XRP on 1D charts, it features dynamic lookbacks, ATR-adjusted thresholds, and SMA confirmation. Signals include strong divergences (triangles) and suppression warnings (crosses). Includes a detailed user guide—try it out and share your feedback!
Setup: Add to XRP 1D chart with defaults (mom_length_base=8, vol_length_base=10). Signals: Red triangle (sell), Green triangle (buy), Orange cross (bear warning), Yellow cross (bull warning). Confirm with 5-day SMA crossovers. See full guide for details!
Disclaimer: This indicator is for educational purposes only, not financial advice. Trading involves risk—use at your discretion.
Momentum Volume Divergence (MVD) Enhanced Indicator User Guide
Version: Pine Script v6
Designed for: TradingView
Recommended Use: XRP on 1-day (1D) chart
Date: March 18, 2025
Author: Herschel with assistance from Grok 3 (xAI)
Overview
The Momentum Volume Divergence (MVD) Enhanced indicator is a powerful tool for identifying price-momentum divergences and momentum suppression patterns on XRP’s 1-day (1D) chart. Plotted below the price chart, it provides clear visual signals to help traders spot potential reversals and trend shifts.
Purpose
Detect divergences between price and momentum for buy/sell opportunities.
Highlight momentum suppression as warnings of fading trends.
Offer actionable trading signals with intuitive markers.
Indicator Components
Main Plot
Volume-Weighted Momentum (vw_mom): Blue line showing momentum adjusted by volume.
Above 0 = bullish momentum.
Below 0 = bearish momentum.
Zero Line: Gray dashed line at 0, separating bullish/bearish zones.
Key Signals
Strong Bearish Divergence:
Marker: Red triangle at the top.
Meaning: Price makes a higher high, but momentum weakens, confirmed by a drop below the 5-day SMA.
Action: Potential sell/short signal.
Strong Bullish Divergence:
Marker: Green triangle at the bottom.
Meaning: Price makes a lower low, but momentum strengthens, confirmed by a rise above the 5-day SMA.
Action: Potential buy/long signal.
Bearish Suppression:
Marker: Orange cross at the top + red background.
Meaning: Strong bullish momentum with low volume in a volume downtrend, suggesting fading strength.
Action: Warning to avoid longs or exit early.
Bullish Suppression:
Marker: Yellow cross at the bottom + green background.
Meaning: Strong bearish momentum with low volume in a volume uptrend, suggesting fading weakness.
Action: Warning to avoid shorts or exit early.
Debug Plots (Optional)
Volume Ratio: Gray line (volume vs. its MA) vs. yellow line (threshold).
Momentum Threshold: Purple lines (positive/negative momentum cutoffs).
Smoothed Momentum: Orange line (raw momentum).
Confirmation SMA: Purple line (price trend confirmation).
Labels
Text labels (e.g., "Bear Div," "Bull Supp") mark detected patterns.
How to Use the Indicator
Step-by-Step Trading Process
1. Monitor the Chart
Load your XRP 1D chart with the indicator applied.
Observe the blue vw_mom line and signal markers.
2. Spot a Signal
Primary Signals: Look for red triangles (strong_bear) or green triangles (strong_bull).
Warnings: Note orange crosses (suppression_bear) or yellow crosses (suppression_bull).
3. Confirm the Signal
For Strong Bullish Divergence (Buy):
Green triangle appears.
Price closes above the 5-day SMA (purple line) and a recent swing high.
Optional: Volume ratio (gray line) exceeds the threshold (yellow line).
For Strong Bearish Divergence (Sell):
Red triangle appears.
Price closes below the 5-day SMA and a recent swing low.
Optional: Volume ratio (gray line) falls below the threshold (yellow line).
4. Enter the Trade
Long:
Buy at the close of the signal bar.
Stop loss: Below the recent swing low or 2 × ATR(14) below entry.
Short:
Sell/short at the close of the signal bar.
Stop loss: Above the recent swing high or 2 × ATR(14) above entry.
5. Manage the Trade
Take Profit:
Aim for a 2:1 or 3:1 risk-reward ratio (e.g., risk $0.05, target $0.10-$0.15).
Or exit when an opposite suppression signal appears (e.g., orange cross for longs).
Trailing Stop:
Move stop to breakeven after a 1:1 RR move.
Trail using the 5-day SMA or 2 × ATR(14).
Early Exit:
Exit if a suppression signal appears against your position (e.g., suppression_bull while short).
6. Filter Out Noise
Avoid trades if a suppression signal precedes a divergence within 2-3 days.
Optional: Add a 50-day SMA on the price chart:
Longs only if price > 50-SMA.
Shorts only if price < 50-SMA.
Example Trades (XRP 1D)
Bullish Trade
Signal: Green triangle (strong_bull) at $0.55.
Confirmation: Price closes above 5-SMA and $0.57 high.
Entry: Buy at $0.58.
Stop Loss: $0.53 (recent low).
Take Profit: $0.63 (2:1 RR) or exit on suppression_bear.
Outcome: Price hits $0.64, exit at $0.63 for profit.
Bearish Trade
Signal: Red triangle (strong_bear) at $0.70.
Confirmation: Price closes below 5-SMA and $0.68 low.
Entry: Short at $0.67.
Stop Loss: $0.71 (recent high).
Take Profit: $0.62 (2:1 RR) or exit on suppression_bull.
Outcome: Price drops to $0.61, exit at $0.62 for profit.
Tips for Success
Combine with Price Levels:
Use support/resistance zones (e.g., weekly pivots) to confirm entries.
Monitor Volume:
Rising volume (gray line above yellow) strengthens signals.
Adjust Sensitivity:
Too many signals? Increase div_strength_threshold to 0.7.
Too few signals? Decrease to 0.3.
Backtest:
Review 20-30 past signals on XRP 1D to assess performance.
Avoid Choppy Markets:
Skip signals during low volatility (tight price ranges).
Troubleshooting
No Signals:
Lower div_strength_threshold to 0.3 or mom_threshold_base to 0.2.
Check if XRP’s volatility is unusually low.
False Signals:
Increase sma_confirm_length to 7 or add a 50-SMA filter.
Indicator Not Loading:
Ensure the script compiles without errors.
Customization (Optional)
Change Colors: Edit color.* values (e.g., color.red to color.purple).
Add Alerts: Use TradingView’s alert menu for "Strong Bearish Divergence Confirmed," etc.
Test Other Assets: Experiment with BTC or ETH, adjusting inputs as needed.
Disclaimer
This indicator is for educational purposes only and not financial advice. Trading involves risk, and past performance does not guarantee future results. Use at your own discretion.
Setup: Use on XRP 1D with defaults (mom_length_base=8, vol_length_base=10). Signals: Red triangle (sell), Green triangle (buy), Orange cross (bear warning), Yellow cross (bull warning). Confirm with 5-day SMA cross. Stop: 2x ATR(14). Profit: 2:1 RR or suppression exit. Full guide available separately!
Volume Predictor [PhenLabs]📊 Volume Predictor
Version: PineScript™ v6
📌 Description
The Volume Predictor is an advanced technical indicator that leverages machine learning and statistical modeling techniques to forecast future trading volume. This innovative tool analyzes historical volume patterns to predict volume levels for upcoming bars, providing traders with valuable insights into potential market activity. By combining multiple prediction algorithms with pattern recognition techniques, the indicator delivers forward-looking volume projections that can enhance trading strategies and market analysis.
🚀 Points of Innovation:
Machine learning pattern recognition using Lorentzian distance metrics
Multi-algorithm prediction framework with algorithm selection
Ensemble learning approach combining multiple prediction methods
Real-time accuracy metrics with visual performance dashboard
Dynamic volume normalization for consistent scale representation
Forward-looking visualization with configurable prediction horizon
🔧 Core Components
Pattern Recognition Engine : Identifies similar historical volume patterns using Lorentzian distance metrics
Multi-Algorithm Framework : Offers five distinct prediction methods with configurable parameters
Volume Normalization : Converts raw volume to percentage scale for consistent analysis
Accuracy Tracking : Continuously evaluates prediction performance against actual outcomes
Advanced Visualization : Displays actual vs. predicted volume with configurable future bar projections
Interactive Dashboard : Shows real-time performance metrics and prediction accuracy
🔥 Key Features
The indicator provides comprehensive volume analysis through:
Multiple Prediction Methods : Choose from Lorentzian, KNN Pattern, Ensemble, EMA, or Linear Regression algorithms
Pattern Matching : Identifies similar historical volume patterns to project future volume
Adaptive Predictions : Generates volume forecasts for multiple bars into the future
Performance Tracking : Calculates and displays real-time prediction accuracy metrics
Normalized Scale : Presents volume as a percentage of historical maximums for consistent analysis
Customizable Visualization : Configure how predictions and actual volumes are displayed
Interactive Dashboard : View algorithm performance metrics in a customizable information panel
🎨 Visualization
Actual Volume Columns : Color-coded green/red bars showing current normalized volume
Prediction Columns : Semi-transparent blue columns representing predicted volume levels
Future Bar Projections : Forward-looking volume predictions with configurable transparency
Prediction Dots : Optional white dots highlighting future prediction points
Reference Lines : Visual guides showing the normalized volume scale
Performance Dashboard : Customizable panel displaying prediction method and accuracy metrics
📖 Usage Guidelines
History Lookback Period
Default: 20
Range: 5-100
This setting determines how many historical bars are analyzed for pattern matching. A longer period provides more historical data for pattern recognition but may reduce responsiveness to recent changes. A shorter period emphasizes recent market behavior but might miss longer-term patterns.
🧠 Prediction Method
Algorithm
Default: Lorentzian
Options: Lorentzian, KNN Pattern, Ensemble, EMA, Linear Regression
Selects the algorithm used for volume prediction:
Lorentzian: Uses Lorentzian distance metrics for pattern recognition, offering excellent noise resistance
KNN Pattern: Traditional K-Nearest Neighbors approach for historical pattern matching
Ensemble: Combines multiple methods with weighted averaging for robust predictions
EMA: Simple exponential moving average projection for trend-following predictions
Linear Regression: Projects future values based on linear trend analysis
Pattern Length
Default: 5
Range: 3-10
Defines the number of bars in each pattern for machine learning methods. Shorter patterns increase sensitivity to recent changes, while longer patterns may identify more complex structures but require more historical data.
Neighbors Count
Default: 3
Range: 1-5
Sets the K value (number of nearest neighbors) used in KNN and Lorentzian methods. Higher values produce smoother predictions by averaging more historical patterns, while lower values may capture more specific patterns but could be more susceptible to noise.
Prediction Horizon
Default: 5
Range: 1-10
Determines how many future bars to predict. Longer horizons provide more forward-looking information but typically decrease accuracy as the prediction window extends.
📊 Display Settings
Display Mode
Default: Overlay
Options: Overlay, Prediction Only
Controls how volume information is displayed:
Overlay: Shows both actual volume and predictions on the same chart
Prediction Only: Displays only the predictions without actual volume
Show Prediction Dots
Default: false
When enabled, adds white dots to future predictions for improved visibility and clarity.
Future Bar Transparency (%)
Default: 70
Range: 0-90
Controls the transparency of future prediction bars. Higher values make future bars more transparent, while lower values make them more visible.
📱 Dashboard Settings
Show Dashboard
Default: true
Toggles display of the prediction accuracy dashboard. When enabled, shows real-time accuracy metrics.
Dashboard Location
Default: Bottom Right
Options: Top Left, Top Right, Bottom Left, Bottom Right
Determines where the dashboard appears on the chart.
Dashboard Text Size
Default: Normal
Options: Small, Normal, Large
Controls the size of text in the dashboard for various display sizes.
Dashboard Style
Default: Solid
Options: Solid, Transparent
Sets the visual style of the dashboard background.
Understanding Accuracy Metrics
The dashboard provides key performance metrics to evaluate prediction quality:
Average Error
Shows the average difference between predicted and actual values
Positive values indicate the prediction tends to be higher than actual volume
Negative values indicate the prediction tends to be lower than actual volume
Values closer to zero indicate better prediction accuracy
Accuracy Percentage
A measure of how close predictions are to actual outcomes
Higher percentages (>70%) indicate excellent prediction quality
Moderate percentages (50-70%) indicate acceptable predictions
Lower percentages (<50%) suggest weaker prediction reliability
The accuracy metrics are color-coded for quick assessment:
Green: Strong prediction performance
Orange: Moderate prediction performance
Red: Weaker prediction performance
✅ Best Use Cases
Anticipate upcoming volume spikes or drops
Identify potential volume divergences from price action
Plan entries and exits around expected volume changes
Filter trading signals based on predicted volume support
Optimize position sizing by forecasting market participation
Prepare for potential volatility changes signaled by volume predictions
Enhance technical pattern analysis with volume projection context
⚠️ Limitations
Volume predictions become less accurate over longer time horizons
Performance varies based on market conditions and asset characteristics
Works best on liquid assets with consistent volume patterns
Requires sufficient historical data for pattern recognition
Sudden market events can disrupt prediction accuracy
Volume spikes may be muted in predictions due to normalization
💡 What Makes This Unique
Machine Learning Approach : Applies Lorentzian distance metrics for robust pattern matching
Algorithm Selection : Offers multiple prediction methods to suit different market conditions
Real-time Accuracy Tracking : Provides continuous feedback on prediction performance
Forward Projection : Visualizes multiple future bars with configurable display options
Normalized Scale : Presents volume as a percentage of maximum volume for consistent analysis
Interactive Dashboard : Displays key metrics with customizable appearance and placement
🔬 How It Works
The Volume Predictor processes market data through five main steps:
1. Volume Normalization:
Converts raw volume to percentage of maximum volume in lookback period
Creates consistent scale representation across different timeframes and assets
Stores historical normalized volumes for pattern analysis
2. Pattern Detection:
Identifies similar volume patterns in historical data
Uses Lorentzian distance metrics for robust similarity measurement
Determines strength of pattern match for prediction weighting
3. Algorithm Processing:
Applies selected prediction algorithm to historical patterns
For KNN/Lorentzian: Finds K nearest neighbors and calculates weighted prediction
For Ensemble: Combines multiple methods with optimized weighting
For EMA/Linear Regression: Projects trends based on statistical models
4. Accuracy Calculation:
Compares previous predictions to actual outcomes
Calculates average error and prediction accuracy
Updates performance metrics in real-time
5. Visualization:
Displays normalized actual volume with color-coding
Shows current and future volume predictions
Presents performance metrics through interactive dashboard
💡 Note:
The Volume Predictor performs optimally on liquid assets with established volume patterns. It’s most effective when used in conjunction with price action analysis and other technical indicators. The multi-algorithm approach allows adaptation to different market conditions by switching prediction methods. Pay special attention to the accuracy metrics when evaluating prediction reliability, as sudden market changes can temporarily reduce prediction quality. The normalized percentage scale makes the indicator consistent across different assets and timeframes, providing a standardized approach to volume analysis.
Gold Price LevelsThis indicator identifies and displays key price levels for gold trading. It highlights important psychological and technical price points that often act as support and resistance levels.
Features
Automatically identifies and displays key price levels ending in 92, 84, 78, 55, 42, 27, and 00
Special emphasis on critical levels ending in 68, 32, and 10 with increased line width
Color-coded visualization: green for levels above current price, red for levels below
Customizable line style, width, and label visibility
Automatically adjusts to different price ranges (works with any gold price)
How to Use
This indicator helps gold traders identify potential support and resistance zones. Watch for price reactions at these levels for potential trade entries, exits, or stop placement. The thicker lines (68, 32, 10) often represent more significant price levels where stronger reactions may occur.
Perfect for both day traders and swing traders looking to optimize their gold trading strategy with key price levels.
Normalized VolumeOVERVIEW
The Normalized Volume (NV) is an attempt at visualizing volume in a format that is more understandable by placing the values on a scale of 0 to 100. 0 in this case is the lowest volume candle available on the chart, and 100 being the highest. Calling a candle “high volume” can be misleading without having something to compare to. For example, in scaling the volume this way we can clearly see that a given candle had 80% of the peak volume or 20%, and gauge the validity of price moves more accurately.
FEATURES
NV by session
Allows user to filter the volume values across 4 different sessions. This can add context to the volume output, because what it high volume during London session may not be high volume relative to New York session.
Overlay plotting
When volume boxes are turned on, this will allow you to toggle how they are plotted.
Color theme
A standard color theme will color the NV based on if the respective candle closed green or red. Selecting variables will color the NV plot based on which range the value falls within.
Session inputs
Activated with the “By session?” Input. Allows user to break the day up into 4 sessions to more accurately gauge volume relative to time of day.
Show Box (X)
Toggles on chart boxes on and off.
Show historical boxes
Will plot prior occurrences of selected volume boxes, deleting them when price fully moves through them in the opposite direction of the initial candle.
Color inputs
Allows for intensive customization in how this tool appears visually.
INTERPRETATION
There are 6 pre-defined ranges that NV can fall within.
NV <= 10
Volume is insignificant
In this range, volume should not be a confirmation in your trading strategy.
NV > 10 and <= 20
Volume is low
In this range, volume should not be a confirmation in your trading strategy.
NV > 20 and <= 40
Volume is fair
In this range, volume should not be the primary confirmation in your trading strategy.
NV > 40 and <= 60
Volume is high
In this range, volume can be the primary confirmation in your trading strategy.
NV > 60 and <= 80
Volume is very high
In this range, volume can be the primary confirmation in your trading strategy.
NV > 80
Volume is extreme
In this range, volume is likely news driven and caution should be taken. High price volatility possible.
To utilize this tool in conjunction with your current strategy, follow the range explanations above section in this section. The higher the NV value, the stronger you can feel about your directional confirmation.
If NV = 100, this means that the highest volume candle occurred up to that point on your selected timeframe. All future data points will be weighed off of this value.
LIMITATIONS
This tool will not load on tickers that do not have volume data, such as VIX.
STRATEGY
The Normalized Volume plot can be used in exactly the same way as you would normally utilize volume in your trading strategy. All we are doing is weighing the volume relative to itself.
Volume boxes can be used as targets to be filled in a similar way to commonly used “fair value gap” strategies. To utilize this strategy, I recommend selecting “Plot to Wicks” in Overlay Plotting and toggling on Show Historical Boxes.
Volume boxes can be used as areas for entry in a similar way to commonly used “order block” strategies. To utilize this strategy, I recommend selecting “Open To Close” in Overlay Plotting.
NOTES
You are able to plot an info label on right side of NV plot using the "Toggle box label" input. When a box is toggled on this label will tell you when the most recent box of that intensity occurred.
This tool is deeply visually customizable, with the ability to adjust line width for plotted boxes, all colors on both box overlays, and all colors on NV panel. Customize it to your liking!
I have a handful of additional features that I plan on adding to this tool in future updates. If there is anything you would like to see added, any bugs you identify, or any strategies you encounter with this tool, I would love to hear from you!
Huge shoutout to @joebaus for assisting in bringing this tool to life, please check out his work here on TradingView!
Pivot P/N VolumesTitle: Pivot P/N Volumes
Short Title: PPNV
Description:
The "Pivot P/N Volumes" indicator is a minimalistic volume analysis tool designed to cut through market noise and highlight key volume events in a separate pane. It strips away conventional volume clutter, focusing on four distinct volume types with clear visual cues, making it ideal for traders seeking actionable insights without distractions.
Key Features:
Blue Bars: Pocket Pivot Volumes (PPV) - Up-day volumes exceeding the highest down-day volume of the last 10 down-days, signaling potential bullish strength.
Orange Bars: Pivot Negative Volumes - Down-day volumes greater than the highest up-day volume of the last 10 up-days, indicating significant bearish pressure.
Red Bars: Down-day volumes above the 50-period EMA of volume, highlighting above-average selling activity.
Green Bars: Up-day volumes above the 50-period EMA of volume, showing above-average buying interest.
Noise: All other volumes are muted as dark grey (down-days) or light grey (up-days) for easy filtering.
Volume Buy/Sell ChartVolume Buy/Sell Chart
This script visualizes the distribution of buying and selling volume within each candlestick, helping traders identify dominant market pressure at a glance. It separates volume into Buy Volume (Green) and Sell Volume (Red) using a unique calculation based on price movement within a candle.
Features:
✅ Customizable Bar Display: Choose to display 5, 10, or 100 bars using a simple dropdown selection.
✅ Buy & Sell Volume Calculation: The script determines buying and selling volume dynamically based on price action within the candle.
✅ Custom Volume Threshold for Alerts: Set a percentage threshold (0–100) to trigger alerts when buy or sell volume exceeds a predefined level.
✅ Color-Coded Histogram:
Green Bars: Represent the estimated buy volume.
Red Bars: Represent the estimated sell volume.
✅ Alerts Integration: Automatically detect strong buy or sell signals when the respective volume percentage exceeds your set threshold.
How It Works:
The script calculates total price movement within a candle.
It then estimates buying and selling volume ratios based on whether the price closes higher or lower than it opened.
Finally, it normalizes the buy/sell volume against the total volume and plots it as a column chart.
Usage Guide:
Add the script to your chart.
Select how many bars to display (5, 10, or 100).
Adjust the Custom Volume Percentage Threshold (default: 75%).
Watch for significant buy/sell volume imbalances that might indicate market turning points!
This tool is great for traders looking to analyze volume flow and market sentiment with a simple yet effective visualization. 🚀
Volume Block Order AnalyzerCore Concept
The Volume Block Order Analyzer is a sophisticated Pine Script strategy designed to detect and analyze institutional money flow through large block trades. It identifies unusually high volume candles and evaluates their directional bias to provide clear visual signals of potential market movements.
How It Works: The Mathematical Model
1. Volume Anomaly Detection
The strategy first identifies "block trades" using a statistical approach:
```
avgVolume = ta.sma(volume, lookbackPeriod)
isHighVolume = volume > avgVolume * volumeThreshold
```
This means a candle must have volume exceeding the recent average by a user-defined multiplier (default 2.0x) to be considered a significant block trade.
2. Directional Impact Calculation
For each block trade identified, its price action determines direction:
- Bullish candle (close > open): Positive impact
- Bearish candle (close < open): Negative impact
The magnitude of impact is proportional to the volume size:
```
volumeWeight = volume / avgVolume // How many times larger than average
blockImpact = (isBullish ? 1.0 : -1.0) * (volumeWeight / 10)
```
This creates a normalized impact score typically ranging from -1.0 to 1.0, scaled by dividing by 10 to prevent excessive values.
3. Cumulative Impact with Time Decay
The key innovation is the cumulative impact calculation with decay:
```
cumulativeImpact := cumulativeImpact * impactDecay + blockImpact
```
This mathematical model has important properties:
- Recent block trades have stronger influence than older ones
- Impact gradually "fades" at rate determined by decay factor (default 0.95)
- Sustained directional pressure accumulates over time
- Opposing pressure gradually counteracts previous momentum
Trading Logic
Signal Generation
The strategy generates trading signals based on momentum shifts in institutional order flow:
1. Long Entry Signal: When cumulative impact crosses from negative to positive
```
if ta.crossover(cumulativeImpact, 0)
strategy.entry("Long", strategy.long)
```
*Logic: Institutional buying pressure has overcome selling pressure, indicating potential upward movement*
2. Short Entry Signal: When cumulative impact crosses from positive to negative
```
if ta.crossunder(cumulativeImpact, 0)
strategy.entry("Short", strategy.short)
```
*Logic: Institutional selling pressure has overcome buying pressure, indicating potential downward movement*
3. Exit Logic: Positions are closed when the cumulative impact moves against the position
```
if cumulativeImpact < 0
strategy.close("Long")
```
*Logic: The original signal is no longer valid as institutional flow has reversed*
Visual Interpretation System
The strategy employs multiple visualization techniques:
1. Color Gradient Bar System:
- Deep green: Strong buying pressure (impact > 0.5)
- Light green: Moderate buying pressure (0.1 < impact ≤ 0.5)
- Yellow-green: Mild buying pressure (0 < impact ≤ 0.1)
- Yellow: Neutral (impact = 0)
- Yellow-orange: Mild selling pressure (-0.1 < impact ≤ 0)
- Orange: Moderate selling pressure (-0.5 < impact ≤ -0.1)
- Red: Strong selling pressure (impact ≤ -0.5)
2. Dynamic Impact Line:
- Plots the cumulative impact as a line
- Line color shifts with impact value
- Line movement shows momentum and trend strength
3. Block Trade Labels:
- Marks significant block trades directly on the chart
- Shows direction and volume amount
- Helps identify key moments of institutional activity
4. Information Dashboard:
- Current impact value and signal direction
- Average volume benchmark
- Count of significant block trades
- Min/Max impact range
Benefits and Use Cases
This strategy provides several advantages:
1. Institutional Flow Detection: Identifies where large players are positioning themselves
2. Early Trend Identification: Often detects institutional accumulation/distribution before major price movements
3. Market Context Enhancement: Provides deeper insight than simple price action alone
4. Objective Decision Framework: Quantifies what might otherwise be subjective observations
5. Adaptive to Market Conditions: Works across different timeframes and instruments by using relative volume rather than absolute thresholds
Customization Options
The strategy allows users to fine-tune its behavior:
- Volume Threshold: How unusual a volume spike must be to qualify
- Lookback Period: How far back to measure average volume
- Impact Decay Factor: How quickly older trades lose influence
- Visual Settings: Labels and line width customization
This sophisticated yet intuitive strategy provides traders with a window into institutional activity, helping identify potential trend changes before they become obvious in price action alone.
JP225 Influence AnalyzerThis tool provides a way to assess how USDJPY and DJIA influence JP225, using standardization and linear regression for quantitative evaluation. It also detects deviations from the linear model and displays the results in a colored table.
Table Structure
Row 1: Current value of USDJPY and its change from the previous bar
Row 2: Current value of DJIA and its change from the previous bar
Row 3: Theoretical value of Nikkei 225 calculated using the least squares method from USDJPY
and DJIA, and its change from the previous bar
Row 4: Current value of the chart symbol (Nikkei 225) and its change from the previous bar
Background Color Meanings
A. Current Value Column (Column 2)
If USDJPY or DJIA significantly contributes to the change in the theoretical value of Nikkei 225, the cell turns blue (increase) or red (decrease). The threshold is 1.5.
If the current value of Nikkei 225 increases, it turns blue; if it decreases, it turns red.
B. Change Value Column (Column 3)
If there is a discrepancy between the change in the theoretical value and the actual change of Nikkei 225, the cell turns yellow (moderate discrepancy: threshold 20) or red (significant discrepancy: threshold 50).
Judgment Based on Current Value Column (Column 2)
If the color of USDJPY or DJIA matches the color of Nikkei 225, that symbol is the main cause.
If there is no match, the main cause is "other factors."
Judgment Based on Change Column (Column 3)
Yellow: Suggests that other factors may be influencing the price.
Red: Strongly indicates that other factors are the main cause.
Parameter Descriptions Parameter Descriptions
symbol_x: Symbol for USDJPY (default: "SAXO:USDJPY")
symbol_y: Symbol for DJIA (default: "OSE:DJIA1!")
threshold_value1: Threshold for determining the influence of USDJPY and DJIA (blue/red color) (default: 1.5)
threshold_value2: Threshold for detecting specific price movements in Nikkei 225 (yellow color) (default: 20)
threshold_value3: Threshold for detecting significant price movements in Nikkei 225 (red color) (default: 50)
data_count: Number of past data points used for calculations (default: 10)
インジケーターの概要
このインジケーターは、日経225先物やCFDの値動きの主な原因が
以下のどれに起因するのかをリアルタイムで表示します
1. ドル円 (USDJPY)
2. ダウ (DJIA)
3. その他の要因（突発的なニュース、225の節目価格への攻防など）
テーブルの構成
1行目 ドル円の現在値と前足からの増減
2行目 ダウの現在値と前足からの増減
3行目 ドル円とダウから最小二乗法で算出した225の理論値とその増減
4行目 チャート銘柄（225）の現在値と前足からの増減
背景色の意味
1. 現在値列 (2列目):ドル円またはダウが225の理論値増減に大きく寄与した場合、
それぞれ青（増加）または赤（減少）に変化。閾値は1.5
225の現在値が増加すれば青、減少すれば赤。
2. 増減値列 (3列目):225の理論値増減と実際の増減が乖離した場合、
黄（中程度：閾値は20）または赤（大幅：閾値は50）に変化。
現在値列(2列目)での判断:
1. 銘柄（ドル円またはダウ）の色が225の色と一致する場合、その銘柄が主な原因。
2. 一致しない場合、主な原因は「その他」。
増減列(3列目)での判断:
黄色 その他の要因が影響している可能性。
赤色 その他の要因が主な原因と強く示唆。
パラメータの説明
symbol_x ドル円のシンボル（デフォルト: "SAXO:USDJPY"）
symbol_y ダウのシンボル（デフォルト: "OSE:DJIA1!"）
threshold_value1 ドル円とダウの影響を判定する(青/赤色)閾値（デフォルト: 1.5）
threshold_value2 225固有の値動きを判定する(黄色)閾値（デフォルト: 20）
threshold_value3 225固有の大きな値動きを判定する(赤色)閾値（デフォルト: 50）
data_count 計算に使用する過去データの本数（デフォルト: 10）
FTB Smart Trader System — Market Maker Levels, EMAs & VectorsThe FTB Trade Engine is an indicator suite I built for myself as a crypto trader. It's designed specifically for trading Institution levels, EMAs, PVSRA Volume Candles, and Session Timings. It helps me spot high probability trade setups without overcomplicating things.
🔑 Features of this Indicator
📌 🔥 Key Session Levels (extend lines in settings as needed)
✅ Weekly High & Low (HOW/LOW) — Automatically plots the previous week's high and low
✅ Daily High & Low (HOD/LOD) — Marks the prior day's range
✅ Asia Session High & Low — Plots the Asian session’s high and low, helping you detect potential breakouts or fakeouts, as Asia often sets the initial high and low of the day.
✅ 50% Asia Level — Automatically calculates and displays the midpoint between Asia’s high and low, an important level for intraday trading.
📌 🔥 Advanced EMA Suite
✅ Includes 10, 20, 50, 200, and 800 EMAs — providing key zones of support, resistance, and trend direction.
👀 Good to know: the break of the 50EMA WITH a vector candle is significant for reversals.
📌 🔥 PVSRA Candles
(👀 IMPORTANT: To properly view PVSRA candles, make sure to UNCHECK all default candle settings — Color Bars, Body, Borders, and Wick — in your chart's candle settings.)
✅ Price, Volume, Support & Resistance Analysis (PVSRA) Candles — These special candles combine price action with volume analysis, color-coded to highlight areas potentially influenced by market makers, institutions, and large players. Perfect for identifying key volume zones and quickly analyzing any coin or pair without switching tools.
Candle Colors Explained:
Bullish Candles:
🟢 Green — 200% increase in volume on bullish moves (strong buyer presence).
🔵 Blue — 150% increase in bullish volume, but may also indicate fatigue or possible reversal.
⚪ White — Normal bullish volume (standard green candles).
Bearish Candles:
🔴 Red — 200% increase in bearish volume compared to the last 10 candles (strong selling).
🟣 Magenta — 150% increase in bearish volume, signaling possible continuation or exhaustion.
⚫ Gray — Normal bearish volume (standard red candles).
Candle Size Alertت وضیحات برای انتشار ابزار در TradingView
🔹 نام ابزار: Candle Size Alert
🔹 توضیحات:
این اندیکاتور برای شناسایی کندلهای بزرگ طراحی شده است. این ابزار میانگین اندازهی ۱۰ کندل گذشته را محاسبه کرده و اگر کندل فعلی ۳ برابر میانگین کندلهای قبلی باشد، یک لیبل هشدار در بالای کندل نمایش میدهد. همچنین میتوان هشدارهای معاملاتی را از طریق alertcondition() فعال کرد.
🔹 ویژگیها:
✅ امکان تغییر تعداد کندلهای محاسبه شده (پیشفرض: ۱۰)
✅ امکان تنظیم ضریب حساسیت (پیشفرض: ۳ برابر)
✅ نمایش لیبل هشدار در بالای کندلهای بزرگ
✅ پشتیبانی از هشدارهای خودکار (AlertCondition)
⚠️ نکته: این اندیکاتور فقط برای تحلیل استفاده میشود و سیگنال خرید یا فروش ارائه نمیدهد.
🔹 Indicator Name: Candle Size Alert
🔹 Description:
This indicator detects large candles by calculating the average size of the last 10 candles. If the current candle is 3 times larger than the average of the previous candles, a warning label appears above the candle. Additionally, automated alerts can be triggered using alertcondition().
🔹 Features:
✅ Adjustable candle count for calculations (default: 10)
✅ Customizable sensitivity multiplier (default: 3x)
✅ Visual alert label above large candles
✅ Supports automated alerts (AlertCondition)
⚠️ Note: This indicator is for analysis purposes only and does not provide buy/sell signals.
Liquidity Hunt SwiftEdgeThe "Liquidity Hunt Dashboard By SwiftEdge" indicator is designed to assist traders in identifying potential liquidity zones by placing a dynamic target line based on swing points and weighted liquidity. It leverages technical analysis tools such as SMA (Simple Moving Average), pivot points, and volume to predict market movements and provides daily statistics on hits and success rate. The target line updates automatically when the price hits it, adapting to the market trend (up, down, or neutral). A dashboard displays the current price, target level, prediction, and trend, making it easy to make informed trading decisions.
Features:
Target Line: A yellow dashed line marks the next expected liquidity level (up to approximately 20 pips away on 1m).
Prediction: Displays "Up (Chasing Sell Liquidity)," "Down (Chasing Buy Liquidity)," or "Neutral" based on trend and liquidity.
Daily Statistics: Tracks hits and success rate, resetting daily.
Trend Indicator: Shows market direction ("Up," "Down," or "Neutral") in the dashboard.
Dynamic Updates: The line moves to a new target level when the price hits the current target.
Recommended Settings for 1-Minute Timeframe:
For Indices (e.g., S&P 500):
Lookback Period: 180 (3 hours to capture more stable swing points).
Max Distance (%): 0.015 (approximately 15 pips, suitable for indices).
Cooldown Period: 5 (stabilizes after hits).
Line Duration: 60 (displays the line for 1 hour).
For Crypto (e.g., BTC/USD):
Lookback Period: 120 (2 hours to capture short-term swing points).
Max Distance (%): 0.024 (approximately 20 pips, suitable for volatile crypto markets).
Cooldown Period: 5.
Line Duration: 60.
For Forex (e.g., EUR/USD):
Lookback Period: 180 (3 hours for greater data density in less volatile markets).
Max Distance (%): 0.012 (approximately 10-12 pips, suitable for forex).
Cooldown Period: 5.
Line Duration: 60.
Guide for Higher Timeframes:
This indicator can be adapted for higher timeframes (e.g., 5m, 15m, 1H) by adjusting the settings to account for larger price movements and slower market dynamics. Follow these steps:
Select Your Timeframe: Switch your chart to the desired timeframe (e.g., 5m, 15m, or 1H).
Adjust Lookback Period: Increase the "Lookback Period" to cover a longer historical period. For example:
5m: Set to 360 (equivalent to 6 hours).
15m: Set to 480 (equivalent to 8 hours).
1H: Set to 720 (equivalent to 12 hours).
Adjust Max Distance (%): Higher timeframes require larger targets to account for bigger price swings. For example:
5m: Increase to 0.05 (approximately 50 pips).
15m: Increase to 0.1 (approximately 100 pips).
1H: Increase to 0.2 (approximately 200 pips).
Adjust Cooldown Period: On higher timeframes, you may want a longer cooldown to avoid frequent updates. For example:
5m: Set to 10.
15m: Set to 15.
1H: Set to 20.
Adjust Line Duration: Extend the duration the line is displayed to match the timeframe. For example:
5m: Set to 120 (equivalent to 10 hours).
15m: Set to 240 (equivalent to 60 hours).
1H: Set to 480 (equivalent to 20 days).
Monitor the Dashboard: The dashboard will still show the target level, prediction, and trend, but the values will now reflect the larger timeframe's dynamics.
Usage Instructions:
Set your chart to a 1-minute timeframe (or follow the higher timeframe guide).
Adjust the settings based on the market and timeframe (see recommendations above).
Monitor the dashboard for the current price, target level, and prediction.
Use the yellow line as a potential entry or exit level, and adjust your strategy based on the trend and statistics.
Notes:
This indicator is intended solely for educational and analytical purposes and should not be considered financial advice.
Test the indicator on a demo account before using it with real funds.
The indicator complies with TradingView guidelines by not providing trading advice, automated trading signals, or guarantees of profit.
Multi-Timeframe Trend Table - DOTANMulti Timeframe Trend Signal table - Wizord_KS
"Multi-Timeframe Trend Table"
This Pine Script indicator displays a table on the TradingView chart that shows the trend direction (Bullish/Bearish) across multiple timeframes. The trend is determined using a Simple Moving Average (SMA).
🔹 Key Features of the Script
Calculates trend direction based on SMA for:
The current timeframe.
10 different user-defined timeframes.
Determines a "Main Signal" based on trend strength:
Counts the number of bullish and bearish trends across all timeframes.
Uses this count to generate an overall market sentiment (Bullish, Bearish, or Neutral).
The strength of the main signal determines its background transparency.
Displays a table in the top-right corner of the chart showing:
The main trend signal (aggregated from multiple timeframes).
The trend for the current timeframe.
The trend for each of the 10 selected timeframes.
Dual SuperTrend w VIX Filter - Strategy [presentTrading]Hey everyone! Haven't been here for a long time. Been so busy again in the past 2 months. I recently started working on analyzing the combination of trend strategy and VIX, but didn't get outstanding results after a few tries. Sharing this tool with all of you in case you have better insights.
█ Introduction and How it is Different
The Dual SuperTrend with VIX Filter Strategy combines traditional trend following with market volatility analysis. Unlike conventional SuperTrend strategies that focus solely on price action, this experimental system incorporates VIX (Volatility Index) as an adaptive filter to create a more context-aware trading approach. By analyzing where current volatility stands relative to historical norms, the strategy adjusts to different market environments rather than applying uniform logic across all conditions.
BTCUSD 6hr Long Short Performance
█ Strategy, How it Works: Detailed Explanation
🔶 Dual SuperTrend Core
The strategy uses two SuperTrend indicators with different sensitivity settings:
- SuperTrend 1: Length = 13, Multiplier = 3.5
- SuperTrend 2: Length = 8, Multiplier = 5.0
The SuperTrend calculation follows this process:
1. ATR = Average of max(High-Low, |High-PreviousClose|, |Low-PreviousClose|) over 'length' periods
2. UpperBand = (High+Low)/2 - (Multiplier * ATR)
3. LowerBand = (High+Low)/2 + (Multiplier * ATR)
Trend direction is determined by:
- If Close > previous LowerBand, Trend = Bullish (1)
- If Close < previous UpperBand, Trend = Bearish (-1)
- Otherwise, Trend = previous Trend
🔶 VIX Analysis Framework
The core innovation lies in the VIX analysis system:
1. Statistical Analysis:
- VIX Mean = SMA(VIX, 252)
- VIX Standard Deviation = StdDev(VIX, 252)
- VIX Z-Score = (Current VIX - VIX Mean) / VIX StdDev
2. **Volatility Bands:
- Upper Band 1 = VIX Mean + (2 * VIX StdDev)
- Upper Band 2 = VIX Mean + (3 * VIX StdDev)
- Lower Band 1 = VIX Mean - (2 * VIX StdDev)
- Lower Band 2 = VIX Mean - (3 * VIX StdDev)
3. Volatility Regimes:
- "Very Low Volatility": VIX < Lower Band 1
- "Low Volatility": Lower Band 1 ≤ VIX < Mean
- "Normal Volatility": Mean ≤ VIX < Upper Band 1
- "High Volatility": Upper Band 1 ≤ VIX < Upper Band 2
- "Extreme Volatility": VIX ≥ Upper Band 2
4. VIX Trend Detection:
- VIX EMA = EMA(VIX, 10)
- VIX Rising = VIX > VIX EMA
- VIX Falling = VIX < VIX EMA
Local performance:
🔶 Entry Logic Integration
The strategy combines trend signals with volatility filtering:
Long Entry Condition:
- Both SuperTrend 1 AND SuperTrend 2 must be bullish (trend = 1)
- AND selected VIX filter condition must be satisfied
Short Entry Condition:
- Both SuperTrend 1 AND SuperTrend 2 must be bearish (trend = -1)
- AND selected VIX filter condition must be satisfied
Available VIX filter rules include:
- "Below Mean + SD": VIX < Lower Band 1
- "Below Mean": VIX < VIX Mean
- "Above Mean": VIX > VIX Mean
- "Above Mean + SD": VIX > Upper Band 1
- "Falling VIX": VIX < VIX EMA
- "Rising VIX": VIX > VIX EMA
- "Any": No VIX filtering
█ Trade Direction
The strategy allows testing in three modes:
1. **Long Only:** Test volatility effects on uptrends only
2. **Short Only:** Examine volatility's impact on downtrends only
3. **Both (Default):** Compare how volatility affects both trend directions
This enables comparative analysis of how volatility regimes impact bullish versus bearish markets differently.
█ Usage
Use this strategy as an experimental framework:
1. Form a hypothesis about how volatility affects trend reliability
2. Configure VIX filters to test your specific hypothesis
3. Analyze performance across different volatility regimes
4. Compare results between uptrends and downtrends
5. Refine your volatility filtering approach based on results
6. Share your findings with the trading community
This framework allows you to investigate questions like:
- Are uptrends more reliable during rising or falling volatility?
- Do downtrends perform better when volatility is above or below its historical average?
- Should different volatility filters be applied to long vs. short positions?
█ Default Settings
The default settings serve as a starting point for exploration:
SuperTrend Parameters:
- SuperTrend 1 (Length=13, Multiplier=3.5): More responsive to trend changes
- SuperTrend 2 (Length=8, Multiplier=5.0): More selective filter requiring stronger trends
VIX Analysis Settings:
- Lookback Period = 252: Establishes a full market cycle for volatility context
- Standard Deviation Bands = 2 and 3 SD: Creates statistically significant regime boundaries
- VIX Trend Period = 10: Balances responsiveness with noise reduction
Default VIX Filter Selection:
- Long Entry: "Above Mean" - Tests if uptrends perform better during above-average volatility
- Short Entry: "Rising VIX" - Tests if downtrends accelerate when volatility is increasing
Feel Free to share your insight below!!!
Whale Buy Activity Detector (Real-Time)Whale Buy Activity Detector (Real-Time)
This indicator helps to identify abnormal spikes in the volume of purchases, which may indicate the activity of large players ("whales"). It analyzes the volume of purchases and compares it with the average volume over a certain period of time. If the volume of purchases exceeds a set threshold, the indicator marks this as potential whale activity.
Basic parameters:
Volume Threshold (x Average): The coefficient by which the current purchase volume must exceed the average volume in order to be considered abnormal. The default value is 2.0, which means that the purchase volume should be 2 times the average volume for the selected time period. This parameter can be adjusted in the range from 1.0 and higher in increments of 0.1.
Example: If you set the value to 1.5, the indicator will mark situations when the volume of purchases exceeds the average volume by 1.5 times.
Lookback Period: The time period used to calculate the average purchase volume. The default value is 20, which means that the average purchase volume will be calculated for the last 20 candles. This parameter can be set in the range from 1 and above.Example: If you set the value to 10, the average purchase volume will be calculated for the last 10 candles.
How to use:
Buy Volume: Shows the volume of purchases on each candle. This is the volume that was sold at a price higher than the opening price of the candle.
Average Buy Volume: The average volume of purchases over a given time period (Lookback Period). This parameter helps to determine the "normal" level of purchase volume.
Whale Buy: Notes abnormal spikes in the volume of purchases, which may indicate the activity of "whales". The indicator draws a mark on the top of the candle when the purchase volume exceeds the threshold set by the Volume Threshold parameter.
Notifications:
The indicator can send notifications when an abnormal volume of purchases is detected. You can set up notifications via the TradingView menu to receive real-time alerts.
Usage example:
If you are trading in a highly volatile market, you can increase the Volume Threshold to filter out small volume spikes.
If you trade in a low-volatility market, you can reduce the Volume Threshold to capture even small anomalies.
Quarterly Theory ICT 02 [TradingFinder] True Open Session 90 Min🔵 Introduction
The Quarterly Theory ICT indicator is an advanced analytical system built on ICT (Inner Circle Trader) concepts and fractal time. It divides time into four quarters (Q1, Q2, Q3, Q4), and is designed based on the consistent repetition of these phases across all trading timeframes (annual, monthly, weekly, daily, and even shorter trading sessions).
Each cycle consists of four distinct phases: the first phase (Q1) is the Accumulation phase, characterized by price consolidation; the second phase (Q2), known as Manipulation or Judas Swing, is marked by initial false movements indicating a potential shift; the third phase (Q3) is Distribution, where price volatility peaks; and the fourth phase (Q4) is Continuation/Reversal, determining whether the previous trend continues or reverses.
🔵 How to Use
The central concept of this strategy is the "True Open," which refers to the actual starting point of each time cycle. The True Open is typically defined at the beginning of the second phase (Q2) of each cycle. Prices trading above or below the True Open serve as a benchmark for predicting the market's potential direction and guiding trading decisions.
The practical application of the Quarterly Theory strategy relies on accurately identifying True Open points across various timeframes.
True Open points are defined as follows :
Yearly Cycle :
Q1: January, February, March
Q2: April, May, June (True Open: April Monthly Open)
Q3: July, August, September
Q4: October, November, December
Monthly Cycle :
Q1: First Monday of the month
Q2: Second Monday of the month (True Open: Daily Candle Open price on the second Monday)
Q3: Third Monday of the month
Q4: Fourth Monday of the month
Weekly Cycle :
Q1: Monday
Q2: Tuesday (True Open: Daily Candle Open Price on Tuesday)
Q3: Wednesday
Q4: Thursday
Daily Cycle :
Q1: 18:00 - 00:00 (Asian session)
Q2: 00:00 - 06:00 (True Open: Start of London Session)
Q3: 06:00 - 12:00 (NY AM)
Q4: 12:00 - 18:00 (NY PM)
90 Min Asian Session :
Q1: 18:00 - 19:30
Q2: 19:30 - 21:00 (True Open at 19:30)
Q3: 21:00 - 22:30
Q4: 22:30 - 00:00
90 Min London Session :
Q1: 00:00 - 01:30
Q2: 01:30 - 03:00 (True Open at 01:30)
Q3: 03:00 - 04:30
Q4: 04:30 - 06:00
90 Min New York AM Session :
Q1: 06:00 - 07:30
Q2: 07:30 - 09:00 (True Open at 07:30)
Q3: 09:00 - 10:30
Q4: 10:30 - 12:00
90 Min New York PM Session :
Q1: 12:00 - 13:30
Q2: 13:30 - 15:00 (True Open at 13:30)
Q3: 15:00 - 16:30
Q4: 16:30 - 18:00
Micro Cycle (22.5-Minute Quarters) : Each 90-minute quarter is further divided into four 22.5-minute sub-segments (Micro Sessions).
True Opens in these sessions are defined as follows :
Asian Micro Session :
True Session Open : 19:30 - 19:52:30
London Micro Session :
T rue Session Open : 01:30 - 01:52:30
New York AM Micro Session :
True Session Open : 07:30 - 07:52:30
New York PM Micro Session :
True Session Open : 13:30 - 13:52:30
By accurately identifying these True Open points across various timeframes, traders can effectively forecast the market direction, analyze price movements in detail, and optimize their trading positions. Prices trading above or below these key levels serve as critical benchmarks for determining market direction and making informed trading decisions.
🔵 Setting
Show True Range : Enable or disable the display of the True Range on the chart, including the option to customize the color.
Extend True Range Line : Choose how to extend the True Range line on the chart, with the following options:
None: No line extension
Right: Extend the line to the right
Left: Extend the line to the left
Both: Extend the line in both directions (left and right)
Show Table : Determines whether the table—which summarizes the phases (Q1 to Q4)—is displayed.
Show More Info : Adds additional details to the table, such as the name of the phase (Accumulation, Manipulation, Distribution, or Continuation/Reversal) or further specifics about each cycle.
🔵 Conclusion
The Quarterly Theory ICT, by dividing time into four distinct quarters (Q1, Q2, Q3, and Q4) and emphasizing the concept of the True Open, provides a structured and repeatable framework for analyzing price action across multiple time frames.
The consistent repetition of phases—Accumulation, Manipulation (Judas Swing), Distribution, and Continuation/Reversal—allows traders to effectively identify recurring price patterns and critical market turning points. Utilizing the True Open as a benchmark, traders can more accurately determine potential directional bias, optimize trade entries and exits, and manage risk effectively.
By incorporating principles of ICT (Inner Circle Trader) and fractal time, this strategy enhances market forecasting accuracy across annual, monthly, weekly, daily, and shorter trading sessions. This systematic approach helps traders gain deeper insight into market structure and confidently execute informed trading decisions.
Advanced Support and Resistance Levels[MAP]Advanced Support and Resistance Levels Indicator
Author
Developed by:
Overview
The "Advanced Support and Resistance Levels" indicator, created, is a sophisticated tool designed for TradingView's Pine Script v6 platform. It identifies and plots key support and resistance levels on a price chart, enhancing technical analysis by incorporating pivot strength, volume weighting, and level decay. The indicator overlays lines, zones, and labels on the chart, providing a visual representation of significant price levels where the market has historically reversed or consolidated.
Purpose
This indicator, authored by , aims to:
Detect significant pivot points (highs and lows) with customizable strength requirements.
Track and rank support/resistance levels based on their recency, volume, and number of touches.
Display these levels as lines and optional zones, with strength-based visual cues (e.g., line thickness and opacity).
Offer flexibility through user-configurable settings to adapt to different trading styles and market conditions.
Features
Pivot Detection:
Identifies high and low pivots using a strength parameter, requiring a specified number of bars on either side where no higher highs or lower lows occur.
Incorporates closing price checks and SMA-based trend confirmation to filter out noise and ensure pivots align with the broader market direction.
Level Management:
Maintains a dynamic array of levels with attributes: price, type (support/resistance), bars since last touch, strength, and volume.
Merges nearby levels within a tolerance percentage, updating prices with a strength-weighted average.
Prunes weaker or older levels when exceeding the maximum allowed, prioritizing those with higher calculated strength.
Strength Calculation:
Combines the number of touches (strength), volume (if enabled), and age decay (if enabled) into a single metric.
Volume weighting uses a logarithmic scale to emphasize high-volume pivots without over-amplifying extreme values.
Age decay reduces the importance of older levels over time, ensuring relevance to current price action.
Visualization:
Draws horizontal lines at each level, with thickness reflecting the number of touches (up to a user-defined maximum).
Optional price zones around levels, sized as a percentage of the price, to indicate areas of influence.
Labels display the level type (S for support, R for resistance), price, and strength score, with position (left or right) customizable.
Line opacity varies with strength, providing a visual hierarchy of level significance.
Plots small triangles at detected pivot points for reference.
Inputs
Lookback Period (lookback, default: 20): Number of bars to consider for trend confirmation via SMA. Range: 5–100.
Pivot Strength (strength, default: 2): Number of bars required on each side of a pivot to confirm it. Range: 1–10.
Price Tolerance % (tolerance, default: 0.5): Percentage range for merging similar levels. Range: 0.1–5.
Max Levels to Show (maxLevels, default: 10): Maximum number of levels displayed. Range: 2–50.
Zone Size % (zoneSizePercent, default: 0.1): Size of the S/R zone as a percentage of the price. Range: 0–1.
Line Width (lineWidth, default: 1): Maximum thickness of level lines. Range: 1–5.
Show Labels (showLabels, default: true): Toggle visibility of level labels.
Label Position (labelPos, default: "Right"): Position of labels ("Left" or "Right").
Level Strength Decay (levelDecay, default: true): Enable gradual reduction in strength for older levels.
Volume Weighting (volumeWeight, default: true): Incorporate volume into level strength calculations.
Support Color (supportColor, default: green): Color for support levels.
Resistance Color (resistColor, default: red): Color for resistance levels.
How It Works
Pivot Detection:
Checks for pivots only after enough bars (2 * strength) have passed.
A high pivot requires strength bars before and after with no higher highs or closes, and a short-term SMA above a long-term SMA.
A low pivot requires strength bars before and after with no lower lows or closes, and a short-term SMA below a long-term SMA.
Level Tracking:
New pivots create levels with initial strength and volume.
Existing levels within tolerance are updated: strength increases, volume takes the maximum value, and price adjusts via a weighted average.
Levels older than lookback * 4 bars with strength below 0.5 are removed.
If the number of levels exceeds maxLevels, the weakest (by calculated strength) are pruned using a selection sort algorithm.
Drawing:
Updates on the last confirmed bar or in real-time.
Lines extend lookback bars left and right from the current bar, with thickness based on touches.
Zones (if enabled) are drawn symmetrically around the level price.
Labels show detailed info, with opacity tied to strength.
Usage
Add to Chart: Apply the indicator to any TradingView chart via the Pine Script editor, as designed by .
Adjust Settings: Customize inputs to match your trading strategy (e.g., increase strength for stronger pivots, adjust tolerance for tighter level merging).
Interpret Levels: Focus on thicker, less transparent lines for stronger levels; use zones to identify potential reversal areas.
Combine with Other Tools: Pair with trend indicators or oscillators for confluence in trading decisions.
Notes
Performance: The indicator uses arrays and sorting, which may slow down on very long charts with many levels. Keep maxLevels reasonable for efficiency.
Accuracy: Enhanced by trend confirmation and volume weighting, making it more reliable than basic S/R indicators, thanks to 's design.
Limitations: Real-time updates may shift levels as new pivots form; historical levels are more stable.
Example Settings
For day trading: lookback=10, strength=1, tolerance=0.3, maxLevels=5.
For swing trading: lookback=50, strength=3, tolerance=0.7, maxLevels=10.
Credits
Author: – Creator of this advanced support and resistance tool, blending precision and customization for traders.
Double Top/Bottom Fractals DetectorDouble Top/Bottom Detector with Williams Fractals (Extended + Early Signal)
This indicator combines the classic Williams Fractals methodology with an enhanced mechanism to detect potential reversal patterns—namely, double tops and double bottoms. It does so by using two separate detection schemes:
Confirmed Fractals for Pattern Formation:
The indicator calculates confirmed fractals using the traditional Williams Fractals rules. A fractal is confirmed if a bar’s high (for an up fractal) or low (for a down fractal) is the highest or lowest compared to a specified number of bars on both sides (default: 2 bars on the left and 2 on the right).
Once a confirmed fractal is identified, its price (high for tops, low for bottoms) and bar index are stored in an internal array (up to the 10 most recent confirmed fractals).
When a new confirmed fractal appears, the indicator compares it with previous confirmed fractals. If the new fractal is within a user-defined maximum bar distance (e.g., 20 bars) and the price difference is within a specified tolerance (default: 0.8%), the indicator assumes that a double top (if comparing highs) or a double bottom (if comparing lows) pattern is forming.
A signal is then generated by placing a label on the chart—SELL for a double top and BUY for a double bottom.
Early Signal Generation:
To capture potential reversals sooner, the indicator also includes an “early signal” mechanism. This uses asymmetric offsets different from the confirmed fractal calculation:
Signal Right Offset: Defines the candidate bar used for early signal detection (default is 1 bar).
Signal Left Offset: Defines the number of bars to the left of the candidate that must confirm the candidate’s price is the extreme (default is 2 bars).
For an early top candidate, the candidate bar’s high must be greater than the highs of the bars specified by the left offset and also higher than the bar immediately to its right. For an early bottom candidate, the corresponding condition applies for lows.
If the early candidate’s price level is within the acceptable tolerance when compared to any of the previously stored confirmed fractals (again, within the allowed bar distance), an early signal is generated—displayed as SELL_EARLY or BUY_EARLY.
The early signal block can be enabled or disabled via a checkbox input, allowing traders to choose whether to use these proactive signals.
Key Parameters:
n:
The number of bars used to confirm a fractal. The fractal is considered valid if the bar’s high (or low) is higher (or lower) than the highs (or lows) of the preceding and following n bars.
maxBarsApart:
The maximum number of bars allowed between two fractals for them to be considered part of the same double top or bottom pattern.
tolerancePercent:
The maximum allowed percentage difference (default: 0.8%) between the high (or low) values of two fractals to qualify them as matching for the pattern.
signalLeftOffset & signalRightOffset:
These parameters define the asymmetric offsets for early signal detection. The left offset (default: 2) specifies how many bars to look back, while the right offset (default: 1) specifies the candidate bar’s position.
earlySignalsEnabled:
A checkbox option that allows users to enable or disable early signal generation. When disabled, the indicator only uses confirmed fractal signals.
How It Works:
Fractal Calculation and Plotting:
The confirmed fractals are calculated using the traditional method, ensuring robust identification by verifying the pattern with a symmetrical offset. These confirmed fractals are plotted on the chart using triangle shapes (upwards for potential double bottoms and downwards for potential double tops).
Pattern Detection:
Upon detection of a new confirmed fractal, the indicator checks up to 10 previous fractals stored in internal arrays. If the new fractal’s high or low is within the tolerance range and close enough in terms of bars to one of the stored fractals, it signifies the formation of a double top or double bottom. A corresponding SELL or BUY label is then placed on the chart.
Early Signal Feature:
If enabled, the early signal block checks for candidate bars based on the defined asymmetric offsets. These candidates are evaluated to see if their high/low levels meet the early confirmation criteria relative to nearby bars. If they also match one of the confirmed fractal levels (within tolerance and bar distance), an early signal is issued with a label (SELL_EARLY or BUY_EARLY) on the chart.
Benefits for Traders:
Timely Alerts:
By combining both confirmed and early signals, the indicator offers a proactive approach to detect reversals sooner, potentially improving entry and exit timing.
Flexibility:
With adjustable parameters (including the option to disable early signals), traders can fine-tune the indicator to better suit different markets, timeframes, and trading styles.
Enhanced Pattern Recognition:
The dual-layered approach (confirmed fractals plus early detection) helps filter out false signals and captures the essential formation of double tops and bottoms more reliably.
