SENEE Buy Sell Signal Easy to use:
Buy signal has appeared >>> open long position
Sell signal has appeared >>> open short position
Green ext signal has appeared >>> close long position
Red ext signal has appeared >>> close short position
The colour is an analysis of the trend:
Green bars >>> Up trend
Red bars >>> Down trend
White bars >>> side way
Blue bars >>> Overbought
Orange bars >>> Oversold
How to be setting:
Candles period is Calculation range >>> up to you
Overbought oversold sensitivity is Sensitivity of calculation of
Overbought and Oversold zone
Signal sensitivity is Sensitivity of calculation of Buy,Sell and ext signals
///// Good luck /////
オシレーター
[blackcat] L2 Gradient RSIVWAPOVERVIEW
The L2 Gradient RSIVWAP indicator offers traders a powerful tool for assessing market conditions by combining Relative Strength Index (RSI) with Volume Weighted Average Price (VWAP). It features dynamic coloring and clear buy/sell signals to enhance decision-making.
Customizable Inputs: Adjust key parameters such as RSI-VWAP length, oversold/overbought levels, and smoothing period.
Gradient Color Visualization: Provides intuitive gradient coloring to represent RSI-VWAP values.
Buy/Sell Indicators: On-chart labels highlight potential buying and selling opportunities.
Transparent Fills: Visually distinguishes overbought and oversold zones without obscuring other data.
Access the TradingView platform and select the chart where you wish to implement the indicator.
Go to “Indicators” in the toolbar and search for “ L2 Gradient RSIVWAP.”
Click “Add to Chart” to integrate the indicator into your chart.
Customize settings via the input options:
Toggle between standard RSI and RSI-based VWAP.
Set preferred lengths and thresholds for RSI-VWAP calculations.
Configure the smoothing period for ALMA.
Performance can vary based on asset characteristics like liquidity and volatility.
Historical backtests do not predict future market behavior accurately.
The ALMA function, developed by Arnaud Legoux, enhances response times relative to simple moving averages.
Buy and sell signals are derived from RSI-VWAP crossovers; consider additional factors before making trades.
Special thanks to Arnaud Legoux for creating the ALMA function.
Relative Strength Indicator## ✨RS✨ by Mars: Advanced Relative Strength Indicator
This indicator solves the primary weakness of traditional RS tools: excessive choppiness and false signals. By combining three calculation methods (ratio, performance, or logarithmic comparison) with dynamic filtering techniques, it identifies true trend changes and stock leadership with significantly higher reliability.
### Key Features:
- Multi-timeframe strength analysis (default 10, 21, 63, and 200-period measurements)
- Dynamic signal line with customizable crossing alerts
- Clear visualization with color-coded fills and special crossover signals
- Reversal detection system using momentum and line convergence
- RSI-like scaling (0-100) for easier interpretation with special crossings on overbought and oversold zones.
### Trading Applications:
- Filter out market noise to identify genuine sector/asset leadership shifts
- Eliminate false signals through the convergence of multiple confirmation factors (momentum, proximity, signal crossovers)
- Detect high-probability reversals only when multiple conditions align, reducing premature entries
- Use special signals (bright triangles) for high-confidence entry/exit points when crossovers occur in extreme zones
- Monitor trend reliability through multi-timeframe RS strength percentages
Unlike conventional RS indicators that produce frequent whipsaws, this tool waits for confluent signals across multiple factors. The combination of smoothed RS readings, signal line convergence, and multi-timeframe analysis creates a comprehensive system for identifying market leadership with dramatically reduced false signals. Perfect for rotation strategies and sector allocation decisions where reliability matters more than frequency.
Video:
Fuzzy SMA with DCTI Confirmation[FibonacciFlux]FibonacciFlux: Advanced Fuzzy Logic System with Donchian Trend Confirmation
Institutional-grade trend analysis combining adaptive Fuzzy Logic with Donchian Channel Trend Intensity for superior signal quality
Conceptual Framework & Research Foundation
FibonacciFlux represents a significant advancement in quantitative technical analysis, merging two powerful analytical methodologies: normalized fuzzy logic systems and Donchian Channel Trend Intensity (DCTI). This sophisticated indicator addresses a fundamental challenge in market analysis – the inherent imprecision of trend identification in dynamic, multi-dimensional market environments.
While traditional indicators often produce simplistic binary signals, markets exist in states of continuous, graduated transition. FibonacciFlux embraces this complexity through its implementation of fuzzy set theory, enhanced by DCTI's structural trend confirmation capabilities. The result is an indicator that provides nuanced, probabilistic trend assessment with institutional-grade signal quality.
Core Technological Components
1. Advanced Fuzzy Logic System with Percentile Normalization
At the foundation of FibonacciFlux lies a comprehensive fuzzy logic system that transforms conventional technical metrics into degrees of membership in linguistic variables:
// Fuzzy triangular membership function with robust error handling
fuzzy_triangle(val, left, center, right) =>
if na(val)
0.0
float denominator1 = math.max(1e-10, center - left)
float denominator2 = math.max(1e-10, right - center)
math.max(0.0, math.min(left == center ? val <= center ? 1.0 : 0.0 : (val - left) / denominator1,
center == right ? val >= center ? 1.0 : 0.0 : (right - val) / denominator2))
The system employs percentile-based normalization for SMA deviation – a critical innovation that enables self-calibration across different assets and market regimes:
// Percentile-based normalization for adaptive calibration
raw_diff = price_src - sma_val
diff_abs_percentile = ta.percentile_linear_interpolation(math.abs(raw_diff), normLookback, percRank) + 1e-10
normalized_diff_raw = raw_diff / diff_abs_percentile
normalized_diff = useClamping ? math.max(-clampValue, math.min(clampValue, normalized_diff_raw)) : normalized_diff_raw
This normalization approach represents a significant advancement over fixed-threshold systems, allowing the indicator to automatically adapt to varying volatility environments and maintain consistent signal quality across diverse market conditions.
2. Donchian Channel Trend Intensity (DCTI) Integration
FibonacciFlux significantly enhances fuzzy logic analysis through the integration of Donchian Channel Trend Intensity (DCTI) – a sophisticated measure of trend strength based on the relationship between short-term and long-term price extremes:
// DCTI calculation for structural trend confirmation
f_dcti(src, majorPer, minorPer, sigPer) =>
H = ta.highest(high, majorPer) // Major period high
L = ta.lowest(low, majorPer) // Major period low
h = ta.highest(high, minorPer) // Minor period high
l = ta.lowest(low, minorPer) // Minor period low
float pdiv = not na(L) ? l - L : 0 // Positive divergence (low vs major low)
float ndiv = not na(H) ? H - h : 0 // Negative divergence (major high vs high)
float divisor = pdiv + ndiv
dctiValue = divisor == 0 ? 0 : 100 * ((pdiv - ndiv) / divisor) // Normalized to -100 to +100 range
sigValue = ta.ema(dctiValue, sigPer)
DCTI provides a complementary structural perspective on market trends by quantifying the relationship between short-term and long-term price extremes. This creates a multi-dimensional analysis framework that combines adaptive deviation measurement (fuzzy SMA) with channel-based trend intensity confirmation (DCTI).
Multi-Dimensional Fuzzy Input Variables
FibonacciFlux processes four distinct technical dimensions through its fuzzy system:
Normalized SMA Deviation: Measures price displacement relative to historical volatility context
Rate of Change (ROC): Captures price momentum over configurable timeframes
Relative Strength Index (RSI): Evaluates cyclical overbought/oversold conditions
Donchian Channel Trend Intensity (DCTI): Provides structural trend confirmation through channel analysis
Each dimension is processed through comprehensive fuzzy sets that transform crisp numerical values into linguistic variables:
// Normalized SMA Deviation - Self-calibrating to volatility regimes
ndiff_LP := fuzzy_triangle(normalized_diff, norm_scale * 0.3, norm_scale * 0.7, norm_scale * 1.1)
ndiff_SP := fuzzy_triangle(normalized_diff, norm_scale * 0.05, norm_scale * 0.25, norm_scale * 0.5)
ndiff_NZ := fuzzy_triangle(normalized_diff, -norm_scale * 0.1, 0.0, norm_scale * 0.1)
ndiff_SN := fuzzy_triangle(normalized_diff, -norm_scale * 0.5, -norm_scale * 0.25, -norm_scale * 0.05)
ndiff_LN := fuzzy_triangle(normalized_diff, -norm_scale * 1.1, -norm_scale * 0.7, -norm_scale * 0.3)
// DCTI - Structural trend measurement
dcti_SP := fuzzy_triangle(dcti_val, 60.0, 85.0, 101.0) // Strong Positive Trend (> ~85)
dcti_WP := fuzzy_triangle(dcti_val, 20.0, 45.0, 70.0) // Weak Positive Trend (~30-60)
dcti_Z := fuzzy_triangle(dcti_val, -30.0, 0.0, 30.0) // Near Zero / Trendless (~+/- 20)
dcti_WN := fuzzy_triangle(dcti_val, -70.0, -45.0, -20.0) // Weak Negative Trend (~-30 - -60)
dcti_SN := fuzzy_triangle(dcti_val, -101.0, -85.0, -60.0) // Strong Negative Trend (< ~-85)
Advanced Fuzzy Rule System with DCTI Confirmation
The core intelligence of FibonacciFlux lies in its sophisticated fuzzy rule system – a structured knowledge representation that encodes expert understanding of market dynamics:
// Base Trend Rules with DCTI Confirmation
cond1 = math.min(ndiff_LP, roc_HP, rsi_M)
strength_SB := math.max(strength_SB, cond1 * (dcti_SP > 0.5 ? 1.2 : dcti_Z > 0.1 ? 0.5 : 1.0))
// DCTI Override Rules - Structural trend confirmation with momentum alignment
cond14 = math.min(ndiff_NZ, roc_HP, dcti_SP)
strength_SB := math.max(strength_SB, cond14 * 0.5)
The rule system implements 15 distinct fuzzy rules that evaluate various market conditions including:
Established Trends: Strong deviations with confirming momentum and DCTI alignment
Emerging Trends: Early deviation patterns with initial momentum and DCTI confirmation
Weakening Trends: Divergent signals between deviation, momentum, and DCTI
Reversal Conditions: Counter-trend signals with DCTI confirmation
Neutral Consolidations: Minimal deviation with low momentum and neutral DCTI
A key innovation is the weighted influence of DCTI on rule activation. When strong DCTI readings align with other indicators, rule strength is amplified (up to 1.2x). Conversely, when DCTI contradicts other indicators, rule impact is reduced (as low as 0.5x). This creates a dynamic, self-adjusting system that prioritizes high-conviction signals.
Defuzzification & Signal Generation
The final step transforms fuzzy outputs into a precise trend score through center-of-gravity defuzzification:
// Defuzzification with precise floating-point handling
denominator = strength_SB + strength_WB + strength_N + strength_WBe + strength_SBe
if denominator > 1e-10
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 -1.0 (Strong Bear) to +1.0 (Strong Bull), with critical threshold zones at ±0.3 (Weak trend) and ±0.7 (Strong trend). The histogram visualization employs intuitive color-coding for immediate trend assessment.
Strategic Applications for Institutional Trading
FibonacciFlux provides substantial advantages for sophisticated trading operations:
Multi-Timeframe Signal Confirmation: Institutional-grade signal validation across multiple technical dimensions
Trend Strength Quantification: Precise measurement of trend conviction with noise filtration
Early Trend Identification: Detection of emerging trends before traditional indicators through fuzzy pattern recognition
Adaptive Market Regime Analysis: Self-calibrating analysis across varying volatility environments
Algorithmic Strategy Integration: Well-defined numerical output suitable for systematic trading frameworks
Risk Management Enhancement: Superior signal fidelity for risk exposure optimization
Customization Parameters
FibonacciFlux offers extensive customization to align with specific trading mandates and market conditions:
Fuzzy SMA Settings: Configure baseline trend identification parameters including SMA, ROC, and RSI lengths
Normalization Settings: Fine-tune the self-calibration mechanism with adjustable lookback period, percentile rank, and optional clamping
DCTI Parameters: Optimize trend structure confirmation with adjustable major/minor periods and signal smoothing
Visualization Controls: Customize display transparency for optimal chart integration
These parameters enable precise calibration for different asset classes, timeframes, and market regimes while maintaining the core analytical framework.
Implementation Notes
For optimal implementation, consider the following guidance:
Higher timeframes (4H+) benefit from increased normalization lookback (800+) for stability
Volatile assets may require adjusted clamping values (2.5-4.0) for optimal signal sensitivity
DCTI parameters should be aligned with chart timeframe (higher timeframes require increased major/minor periods)
The indicator performs exceptionally well as a trend filter for systematic trading strategies
Acknowledgments
FibonacciFlux builds upon the pioneering work of Donovan Wall in Donchian Channel Trend Intensity analysis. The normalization approach draws inspiration from percentile-based statistical techniques in quantitative finance. This indicator is shared for educational and analytical purposes under Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license.
Past performance does not guarantee future results. All trading involves risk. This indicator should be used as one component of a comprehensive analysis framework.
Shout out @DonovanWall
Advanced Trend Indicator🚀 Overview:
The Advanced Trend Indicator is a powerful and reliable tool designed to identify strong market trends with high accuracy. It works seamlessly across stocks, indices, commodities, and cryptocurrencies, making it a versatile asset for traders of all levels.
🎯 Key Features:
✅ EMA Crossover with ADX Filter - Confirms strong trend momentum before signaling entry.
✅ Buy & Sell Signals - Clear and visually appealing labels for trade execution.
✅ Dynamic Background Coloring - Instantly highlights bullish and bearish market conditions.
✅ Trend Strength Meter - Histogram visualization to gauge market direction.
✅ Minimalist & Non-Cluttering Design - Ensures a smooth and distraction-free trading experience.
📊 How It Works:
🔹 Fast & Slow EMA Crossover: The script uses a 9-period Exponential Moving Average (EMA) and a 21-period EMA to detect bullish and bearish trends.
🔹 ADX Confirmation: A 14-period Average Directional Index (ADX) ensures only strong trends generate signals, reducing false entries.
🔹 Buy Signal: Triggered when the fast EMA crosses above the slow EMA AND ADX confirms strong trend strength.
🔹 Sell Signal: Triggered when the fast EMA crosses below the slow EMA AND ADX confirms bearish momentum.
🔹 Trend Strength Meter: Displays bullish (lime) or bearish (maroon) trend intensity for quick visual insights.
🎨 Visual Enhancements:
Buy Signal 🟢: "BUY ✅" label appears at the lowest point of the candle.
Sell Signal 🔴: "SELL ❌" label appears at the highest point of the candle.
Trend Strength Box: Indicates whether the market is in a Strong Uptrend 🔼, Strong Downtrend 🔽, or Neutral 🔍.
Adaptive Background Color: Greenish shade for bullish conditions, reddish shade for bearish conditions.
📈 Best Used On:
📌 Intraday & Swing Trading (Optimized for 5m, 15m, 1H, 4H, and Daily charts)
📌 Works on All Assets (Equities, Forex, Commodities, Indices, and Crypto)
⚡ Why Choose This Indicator?
✅ High Accuracy & Low False Signals 📊
✅ Lightweight & Lag-Free Performance 🚀
✅ Customizable & User-Friendly 🎛️
🚀 Start Trading Smarter with the Advanced Trend Indicator Today! 📉📈
Bitcoin Polynomial Regression ModelThis is the main version of the script. Click here for the Oscillator part of the script.
💡Why this model was created:
One of the key issues with most existing models, including our own Bitcoin Log Growth Curve Model , is that they often fail to realistically account for diminishing returns. As a result, they may present overly optimistic bull cycle targets (hence, we introduced alternative settings in our previous Bitcoin Log Growth Curve Model).
This new model however, has been built from the ground up with a primary focus on incorporating the principle of diminishing returns. It directly responds to this concept, which has been briefly explored here .
📉The theory of diminishing returns:
This theory suggests that as each four-year market cycle unfolds, volatility gradually decreases, leading to more tempered price movements. It also implies that the price increase from one cycle peak to the next will decrease over time as the asset matures. The same pattern applies to cycle lows and the relationship between tops and bottoms. In essence, these price movements are interconnected and should generally follow a consistent pattern. We believe this model provides a more realistic outlook on bull and bear market cycles.
To better understand this theory, the relationships between cycle tops and bottoms are outlined below:https://www.tradingview.com/x/7Hldzsf2/
🔧Creation of the model:
For those interested in how this model was created, the process is explained here. Otherwise, feel free to skip this section.
This model is based on two separate cubic polynomial regression lines. One for the top price trend and another for the bottom. Both follow the general cubic polynomial function:
ax^3 +bx^2 + cx + d.
In this equation, x represents the weekly bar index minus an offset, while a, b, c, and d are determined through polynomial regression analysis. The input (x, y) values used for the polynomial regression analysis are as follows:
Top regression line (x, y) values:
113, 18.6
240, 1004
451, 19128
655, 65502
Bottom regression line (x, y) values:
103, 2.5
267, 211
471, 3193
676, 16255
The values above correspond to historical Bitcoin cycle tops and bottoms, where x is the weekly bar index and y is the weekly closing price of Bitcoin. The best fit is determined using metrics such as R-squared values, residual error analysis, and visual inspection. While the exact details of this evaluation are beyond the scope of this post, the following optimal parameters were found:
Top regression line parameter values:
a: 0.000202798
b: 0.0872922
c: -30.88805
d: 1827.14113
Bottom regression line parameter values:
a: 0.000138314
b: -0.0768236
c: 13.90555
d: -765.8892
📊Polynomial Regression Oscillator:
This publication also includes the oscillator version of the this model which is displayed at the bottom of the screen. The oscillator applies a logarithmic transformation to the price and the regression lines using the formula log10(x) .
The log-transformed price is then normalized using min-max normalization relative to the log-transformed top and bottom regression line with the formula:
normalized price = log(close) - log(bottom regression line) / log(top regression line) - log(bottom regression line)
This transformation results in a price value between 0 and 1 between both the regression lines. The Oscillator version can be found here.
🔍Interpretation of the Model:
In general, the red area represents a caution zone, as historically, the price has often been near its cycle market top within this range. On the other hand, the green area is considered an area of opportunity, as historically, it has corresponded to the market bottom.
The top regression line serves as a signal for the absolute market cycle peak, while the bottom regression line indicates the absolute market cycle bottom.
Additionally, this model provides a predicted range for Bitcoin's future price movements, which can be used to make extrapolated predictions. We will explore this further below.
🔮Future Predictions:
Finally, let's discuss what this model actually predicts for the potential upcoming market cycle top and the corresponding market cycle bottom. In our previous post here , a cycle interval analysis was performed to predict a likely time window for the next cycle top and bottom:
In the image, it is predicted that the next top-to-top cycle interval will be 208 weeks, which translates to November 3rd, 2025. It is also predicted that the bottom-to-top cycle interval will be 152 weeks, which corresponds to October 13th, 2025. On the macro level, these two dates align quite well. For our prediction, we take the average of these two dates: October 24th 2025. This will be our target date for the bull cycle top.
Now, let's do the same for the upcoming cycle bottom. The bottom-to-bottom cycle interval is predicted to be 205 weeks, which translates to October 19th, 2026, and the top-to-bottom cycle interval is predicted to be 259 weeks, which corresponds to October 26th, 2026. We then take the average of these two dates, predicting a bear cycle bottom date target of October 19th, 2026.
Now that we have our predicted top and bottom cycle date targets, we can simply reference these two dates to our model, giving us the Bitcoin top price prediction in the range of 152,000 in Q4 2025 and a subsequent bottom price prediction in the range of 46,500 in Q4 2026.
For those interested in understanding what this specifically means for the predicted diminishing return top and bottom cycle values, the image below displays these predicted values. The new values are highlighted in yellow:
And of course, keep in mind that these targets are just rough estimates. While we've done our best to estimate these targets through a data-driven approach, markets will always remain unpredictable in nature. What are your targets? Feel free to share them in the comment section below.
RSI Price LadderDisplay the 14-period RSI scale on the price chart, including:
+ Key RSI 14 levels.
+ Current values of RSI 14, 45-period WMA, and 9-period EMA.
Donate (buy me a coffee): i.imgur.com
Cyclical Momentum PivotsCYCLICAL MOMENTUM PIVOTS
Overview
Cyclical Momentum Pivots is a streamlined indicator blending Cyclic Smoothed RSI (cRSI) with dynamic momentum detection to pinpoint high-probability trading pivots across stocks, forex, crypto, and more. Built on an adaptive cycle engine, it tracks market rhythm, delivering clear signals for momentum shifts and cyclic reversals, with an optional Hurst-style pivot forecast projected ahead of time. Powered by Pine Script v6, it uses lazy evaluation for real-time efficiency and precision.
How It Works
Momentum Signals (Green/Red Triangles)
Green Triangles (Below Bars): Trigger on volume spikes (default >2x 10-period SMA) with price surges (default ≥1.5%) or volume momentum (>20% over 5 bars).
Red Triangles (Above Bars): Same conditions with price drops.
Dynamic Tuning: Thresholds adjust via volatility (ATR, volume std dev) and cycle strength—stronger signals when cycles deviate far from a dynamic mean.
cRSI Band Crossovers (Turquoise/Purple Diamonds)
Turquoise Diamonds (Below Bars): cRSI crosses up through the low band—potential bullish pivot from oversold.
Purple Diamonds (Above Bars): cRSI crosses down through the high band—bearish pivot from overbought.
cRSI 25% Level Signals (Yellow Markers)
Yellow X (Above Bars): cRSI drops below 25% under the high band with a price decline—early bearish momentum cue.
Yellow O (Below Bars): cRSI rises above 25% over the low band with a price increase—early bullish momentum hint.
Cycle Momentum Signals (Green/Red Circles)
Green Circles (Below Bars): Cycle crosses above its dynamic mean—potential bullish acceleration.
Red Circles (Above Bars): Cycle dips below the mean—potential bearish slowdown.
Why Shorter Cycles Are Bearish, Longer Bullish: Shorter cycles (below mean) signal rapid swings—often bearish, reflecting seller-driven volatility. Longer cycles (above mean) indicate sustained trends—typically bullish, driven by buyer confidence.
Dynamic Cycle Length & Pivot Forecast
Calculation: Detects peaks/troughs over an adaptive window (scaled by smoothed cycle and sensitivity), averages distances, and smooths with an EMA (default 5). Clamped 10–40 bars. Dynamic mean adjusts to cycle length (default 2x multiplier).
Display: White number (e.g., "18") on cycle changes—off by default, toggle on via settings. Optional gray label (e.g., "P+10") forecasts bars until the next pivot, based on Hurst cycle analysis—off by default, toggle on via settings.
Hurst Pivot Forecast: Uses the average pivot period (full cycle, e.g., peak-to-peak or trough-to-trough) to predict the next pivot from the last cycle shift. Half-cycle (e.g., avgPivotPeriod / 2) marks potential midpoints, full cycle (default) targets the next major pivot, and 2x cycle (e.g., avgPivotPeriod * 2) forecasts longer-term turns—adjustable via sensitivity and multiplier settings for custom timing.
Key Features
Adaptive Cycle Engine: Peak/trough distances, smoothed with an EMA, scaled by sensitivity (default 1.0)—locks onto market rhythm.
Cycle Strength: Signals amplify with deviation from a dynamic mean—tighter thresholds in long cycles, looser in short ones.
Pivot Forecast: Optional Hurst-inspired prediction shows bars until the next pivot—enhances planning without clutter.
User Controls: Tune smoothing period (default 5), window sensitivity (0.5–2.0), and mean multiplier (1.0–5.0) for your market.
v6 Efficiency: Lazy evaluation optimizes conditions (e.g., momentumSignal and currentPriceChange > 0) for real-time precision.
Usage Tips
Timeframes: Scales from 5M to 1D—tweak settings for speed or stability.
Assets: Universal—adjust thresholds for volatility (e.g., 2.5 for crypto, 1.5 for forex).
Confirmation: Pair with support/resistance—e.g., green triangle + green circle = strong bullish pivot; red diamond + red circle = bearish pivot. Watch forecast (e.g., "P+5") for timing entries/exits.
Backtesting: Test historically—cycle strength and forecast boost accuracy in trending vs. ranging markets.
Settings
Use Auto Dominant Cycle Length: Enable (default) for adaptive cycles; disable for fixed (default 20).
Example: Enable for crypto’s wild swings; disable and set 30 for stable stocks—locks cycle to asset pace.
Base Volume Threshold: Default 2.0—raise for stricter signals, lower for more.
Example: 2.5 cuts noise in BTC/USD, 1.5 catches more in SPY—tunes signal frequency.
Base Price Change % Threshold: Default 1.5%—adjust for asset volatility.
Example: 2% for high-beta stocks, 1% for forex—matches price action scale.
Volume Momentum Lookback: Default 5—shorten for sensitivity, lengthen for stability.
Example: 3 for 5M scalping, 10 for 1D swings—sharpens momentum detection.
Show Cycle Labels: Disable (default)—enable to see cycle length changes.
Example: Enable on 1H for cycle tracking, disable on 5M for cleaner charts—reduces visual noise.
Show Pivot Forecast: Disable (default)—enable for Hurst-style next-pivot countdown.
Example: Enable on 4H for swing planning (e.g., "P+20"), disable on 15M for focus—adds timing insight.
Cycle Smoothing EMA Period: Default 5—faster (3) for volatility, slower (10) for trends.
Example: 3 smooths fast XRP cycles, 10 steadies SPX trends—reduces erratic signals.
Window Sensitivity: Default 1.0—lower (0.5) for tighter detection, higher (1.5) for broader cycles.
Example: 0.8 narrows for ETH’s chop, 1.2 widens for gold’s slow waves—tunes peak/trough precision.
Mean Multiplier: Default 2.0—shorter (1.5) for responsiveness, longer (3.0) for broader context.
Example: 1.5 tightens signals in 15M forex, 3.0 broadens for 1D indices—shifts momentum circle and forecast timing.
Show cRSI Band Crossovers: Enable (default) for cRSI signals; disable for simplicity.
Example: Enable for reversal plays, disable for momentum focus—cuts clutter.
Why It Stands Out
Cyclical Momentum Pivots’ auto-adaptive cycle—smoothed, strength-weighted, and dynamically averaged—tracks market shifts, delivering clear, actionable pivot signals with optional Hurst-style forecasting ahead of time. v6’s lazy evaluation ensures every trigger is computed efficiently, making it a go-to for traders seeking precision in momentum and reversals.
Bitcoin Polynomial Regression OscillatorThis is the oscillator version of the script. Click here for the other part of the script.
💡Why this model was created:
One of the key issues with most existing models, including our own Bitcoin Log Growth Curve Model , is that they often fail to realistically account for diminishing returns. As a result, they may present overly optimistic bull cycle targets (hence, we introduced alternative settings in our previous Bitcoin Log Growth Curve Model).
This new model however, has been built from the ground up with a primary focus on incorporating the principle of diminishing returns. It directly responds to this concept, which has been briefly explored here .
📉The theory of diminishing returns:
This theory suggests that as each four-year market cycle unfolds, volatility gradually decreases, leading to more tempered price movements. It also implies that the price increase from one cycle peak to the next will decrease over time as the asset matures. The same pattern applies to cycle lows and the relationship between tops and bottoms. In essence, these price movements are interconnected and should generally follow a consistent pattern. We believe this model provides a more realistic outlook on bull and bear market cycles.
To better understand this theory, the relationships between cycle tops and bottoms are outlined below:https://www.tradingview.com/x/7Hldzsf2/
🔧Creation of the model:
For those interested in how this model was created, the process is explained here. Otherwise, feel free to skip this section.
This model is based on two separate cubic polynomial regression lines. One for the top price trend and another for the bottom. Both follow the general cubic polynomial function:
ax^3 +bx^2 + cx + d.
In this equation, x represents the weekly bar index minus an offset, while a, b, c, and d are determined through polynomial regression analysis. The input (x, y) values used for the polynomial regression analysis are as follows:
Top regression line (x, y) values:
113, 18.6
240, 1004
451, 19128
655, 65502
Bottom regression line (x, y) values:
103, 2.5
267, 211
471, 3193
676, 16255
The values above correspond to historical Bitcoin cycle tops and bottoms, where x is the weekly bar index and y is the weekly closing price of Bitcoin. The best fit is determined using metrics such as R-squared values, residual error analysis, and visual inspection. While the exact details of this evaluation are beyond the scope of this post, the following optimal parameters were found:
Top regression line parameter values:
a: 0.000202798
b: 0.0872922
c: -30.88805
d: 1827.14113
Bottom regression line parameter values:
a: 0.000138314
b: -0.0768236
c: 13.90555
d: -765.8892
📊Polynomial Regression Oscillator:
This publication also includes the oscillator version of the this model which is displayed at the bottom of the screen. The oscillator applies a logarithmic transformation to the price and the regression lines using the formula log10(x) .
The log-transformed price is then normalized using min-max normalization relative to the log-transformed top and bottom regression line with the formula:
normalized price = log(close) - log(bottom regression line) / log(top regression line) - log(bottom regression line)
This transformation results in a price value between 0 and 1 between both the regression lines.
🔍Interpretation of the Model:
In general, the red area represents a caution zone, as historically, the price has often been near its cycle market top within this range. On the other hand, the green area is considered an area of opportunity, as historically, it has corresponded to the market bottom.
The top regression line serves as a signal for the absolute market cycle peak, while the bottom regression line indicates the absolute market cycle bottom.
Additionally, this model provides a predicted range for Bitcoin's future price movements, which can be used to make extrapolated predictions. We will explore this further below.
🔮Future Predictions:
Finally, let's discuss what this model actually predicts for the potential upcoming market cycle top and the corresponding market cycle bottom. In our previous post here , a cycle interval analysis was performed to predict a likely time window for the next cycle top and bottom:
In the image, it is predicted that the next top-to-top cycle interval will be 208 weeks, which translates to November 3rd, 2025. It is also predicted that the bottom-to-top cycle interval will be 152 weeks, which corresponds to October 13th, 2025. On the macro level, these two dates align quite well. For our prediction, we take the average of these two dates: October 24th 2025. This will be our target date for the bull cycle top.
Now, let's do the same for the upcoming cycle bottom. The bottom-to-bottom cycle interval is predicted to be 205 weeks, which translates to October 19th, 2026, and the top-to-bottom cycle interval is predicted to be 259 weeks, which corresponds to October 26th, 2026. We then take the average of these two dates, predicting a bear cycle bottom date target of October 19th, 2026.
Now that we have our predicted top and bottom cycle date targets, we can simply reference these two dates to our model, giving us the Bitcoin top price prediction in the range of 152,000 in Q4 2025 and a subsequent bottom price prediction in the range of 46,500 in Q4 2026.
For those interested in understanding what this specifically means for the predicted diminishing return top and bottom cycle values, the image below displays these predicted values. The new values are highlighted in yellow:
And of course, keep in mind that these targets are just rough estimates. While we've done our best to estimate these targets through a data-driven approach, markets will always remain unpredictable in nature. What are your targets? Feel free to share them in the comment section below.
RSI + MA9 + WMA45RSI 14 with EMA 9 & WMA 45, including Price ladder on RSI chart.
Donate (buy me a coffee): i.imgur.com
OG ATR (Average True Range 14 RMA) Range [Elite Edition]📏 OG ATR Sniper Range
Description:
This indicator is your secret weapon for mastering volatility. It calculates the Average True Range (ATR) using a smooth RMA method, giving you a clean view of price volatility per candle — crucial for scalping, sniper entries, stop-loss precision, and targeting.
🔥 What Makes It Elite:
✅ ATR (14 RMA): Clean, stable volatility measure.
✅ Live Value Display: See the real-time ATR printed directly on your chart.
✅ Sniper Zones:
🔹 0.35 = Minimum ideal scalp range
🔹 0.45 = High-volatility play zone (momentum surges)
🧠 Pro Use Tips:
Use when ATR is between 0.35–0.45 to target fast breakout moves.
Combine with SuperTrend, VWAP, EMA stack, or Fib levels for confluence.
Great for AMEX:SPY , QQQ, futures, and options scalpers.
Built for elite scalpers and intraday traders. If you're serious about entries, exits, and timing — this is your rangefinder.
Enhanced OscillatorThis Pine Script indicator, "Enhanced Oscillator," uniquely combines a normalized and smoothed momentum oscillator along with a k-Nearest Neighbors (k-NN) approximation and Hull Moving Average signal line. It's designed to help identify potential overbought and oversold conditions and generate crossover buy/sell trading signals. The addition of the k-NN algorithm and hull moving average signal reduces the oscillator's noise and provides an alternate view of directional momentum.
Usage
Momentum oscillator
The momentum oscillator alone can be used to identify overbought/oversold conditions and generate buy/sell signals.
Price is overbought when the oscillator crosses above 1.00
Price is oversold when the oscillator crosses below 1.00
Directional momentum is slowing when the oscillator moves toward 0 and could indicate a reversal in direction
Crossover above zero is a buy signal
Crossover below zero is a sell signal
K-NN approximation and Hull Moving Average signal line
The K-NN approximation gives a clearer representation of increasing or decreasing momentum by reducing noise of the oscillator. Traders should use the K-NN and signal line in tandem with the momentum oscillator to look for crossovers that can often precede buy/sell signals provided by the main oscillator.
Plots
The momentum oscillator (White/red area)
k-NN Smoothed Ultimate Oscillator (red line)
Signal Line: The HMA signal line (grey line)
Overbought/Oversold Lines: Lines at 1 and -1 indicating potential overbought and oversold conditions.
Zero Line: A horizontal line at zero
**Note:** This script is for informational purposes only and should not be considered financial advice. Always perform your own due diligence and risk management before making any trading decisions.
OG Trend Meter (1m, 5m, 15m, 30m) [Elite Edition]📈 Description for Publishing:
🧭 OG Trend Meter
Description:
This multi-timeframe trend scanner is designed for sniper traders who demand alignment across short-term and macro flow. It tracks EMA 8 vs EMA 21 crossovers across key intraday timeframes and visually displays the overall bias — helping you avoid chop and trade only in clean conditions.
🔥 What You Get:
✅ Trend Strength Visuals across:
1 Minute
5 Minute
15 Minute
30 Minute
✅ Clear Bullish (Green), Bearish (Red), or Neutral (Gray) sentiment blocks
✅ Designed for scalping AMEX:SPY , QQQ, options, and futures
🧠 Pro Tips:
✅ All Green = High confidence calls
✅ All Red = Confident put setup
⚠️ Mixed/Gray = Wait for confirmation
Stack with: SuperTrend + QQE + ATR + VWAP + Fibs
Created by OG WEALTH — sniper entries only. No guessing. No bias. Just flow.
OG QQE MOD [Elite Momentum Edition]📊 QQE MOD
Description:
This upgraded RSI-based indicator gives you early trend reversals and momentum clarity like no other. A favorite among elite scalpers and swing traders, QQE MOD offers smoothed momentum with precision entries and exits far cleaner than traditional RSI.
🔥 What You Get:
✅ Smoothed RSI (QQE) with Signal Line
✅ Clear Bull/Bear Background Color Zones
✅ Visual RSI/Signal Crossovers
✅ Built-in Midline (50) for confirmation
✅ Trend-ready for scalps, day trades, or swing momentum
🧠 Pro Use Tips:
RSI > Signal Line = Bullish momentum (Calls)
RSI < Signal Line = Bearish momentum (Puts)
Use with:
SuperTrend
Trend Meter
VWAP + Volume Power Suite
EMA stack + Fib confluence
OG WEALTH made this for elite timing. Fade the fakeouts. Ride the real moves.
Bitcoin Power LawThis is the main body version of the script. The Oscillator version can be found here .
Firstly, we would like to give credit to @apsk32 and @x_X_77_X_x as part of the code originates from their work. Additionally, @apsk32 is widely credited with applying the Power Law concept to Bitcoin and popularizing this model within the crypto community. Additionally, the visual layout is fully inspired by @apsk32's designs, and we think it looks amazing. So much so that we had to turn it into a TradingView script. Thank you!
Understanding the Bitcoin Power Law Model
Also called the Long-Term Bitcoin Power Law Model. The Bitcoin Power Law model tries to capture and predict Bitcoin's price growth over time. It assumes that Bitcoin's price follows an exponential growth pattern, where the price increases over time according to a mathematical relationship.
By fitting a power law to historical data, the model creates a trend line that represents this growth. It then generates additional parallel lines (support and resistance lines) to show potential price boundaries, helping to visualize where Bitcoin’s price could move within certain ranges.
In simple terms, the model helps us understand Bitcoin's general growth trajectory and provides a framework to visualize how its price could behave over the long term.
The Bitcoin Power Law has the following function:
Power Law = 10^(a + b * log10(d))
Consisting of the following parameters:
a: Power Law Intercept (default: -17.668).
b: Power Law Slope (default: 5.926).
d: Number of days since a reference point(calculated by counting bars from the reference point with an offset).
Explanation of the a and b parameters:
Roughly explained, the optimal values for the a and b parameters are determined through a process of linear regression on a log-log scale (after applying a logarithmic transformation to both the x and y axes). On this log-log scale, the power law relationship becomes linear, making it possible to apply linear regression. The best fit for the regression is then evaluated using metrics like the R-squared value, residual error analysis, and visual inspection. This process can be quite complex and is beyond the scope of this post.
Applying vertical shifts to generate the other lines:
Once the initial power-law is created, additional lines are generated by applying a vertical shift . This shift is achieved by adding a specific number of days (or years in case of this script) to the d-parameter. This creates new lines perfectly parallel to the initial power law with an added vertical shift, maintaining the same slope and intercept.
In the case of this script, shifts are made by adding +365 days, +2 * 365 days, +3 * 365 days, +4 * 365 days, and +5 * 365 days, effectively introducing one to five years of shifts. This results in a total of six Power Law lines, as outlined below (From lowest to highest):
Base Power Law Line (no shift)
1-year shifted line
2-year shifted line
3-year shifted line
4-year shifted line
5-year shifted line
The six power law lines:
Bitcoin Power Law Oscillator
This publication also includes the oscillator version of the Bitcoin Power Law. This version applies a logarithmic transformation to the price, Base Power Law Line, and 5-year shifted line using the formula log10(x) .
The log-transformed price is then normalized using min-max normalization relative to the log-transformed Base Power Law Line and 5-year shifted line with the formula:
normalized price = log(close) - log(Base Power Law Line) / log(5-year shifted line) - log(Base Power Law Line)
Finally, the normalized price was multiplied by 5 to map its value between 0 and 5, aligning with the shifted lines.
Interpretation of the Bitcoin Power Law Model:
The shifted Power Law lines provide a framework for predicting Bitcoin's future price movements based on historical trends. These lines are created by applying a vertical shift to the initial Power Law line, with each shifted line representing a future time frame (e.g., 1 year, 2 years, 3 years, etc.).
By analyzing these shifted lines, users can make predictions about minimum price levels at specific future dates. For example, the 5-year shifted line will act as the main support level for Bitcoin’s price in 5 years, meaning that Bitcoin’s price should not fall below this line, ensuring that Bitcoin will be valued at least at this level by that time. Similarly, the 2-year shifted line will serve as the support line for Bitcoin's price in 2 years, establishing that the price should not drop below this line within that time frame.
On the other hand, the 5-year shifted line also functions as an absolute resistance , meaning Bitcoin's price will not exceed this line prior to the 5-year mark. This provides a prediction that Bitcoin cannot reach certain price levels before a specific date. For example, the price of Bitcoin is unlikely to reach $100,000 before 2021, and it will not exceed this price before the 5-year shifted line becomes relevant. After 2028, however, the price is predicted to never fall below $100,000, thanks to the support established by the shifted lines.
In essence, the shifted Power Law lines offer a way to predict both the minimum price levels that Bitcoin will hit by certain dates and the earliest dates by which certain price points will be reached. These lines help frame Bitcoin's potential future price range, offering insight into long-term price behavior and providing a guide for investors and analysts. Lets examine some examples:
Example 1:
In Example 1 it can be seen that point A on the 5-year shifted line acts as major resistance . Also it can be seen that 5 years later this price level now corresponds to the Base Power Law Line and acts as a major support (Note: Vertical yearly grid lines have been added for this purpose👍).
Example 2:
In Example 2, the price level at point C on the 3-year shifted line becomes a major support three years later at point C, now aligning with the Base Power Law Line.
Finally, let's explore some future price predictions, as this script provides projections on the weekly timeframe :
Example 3:
In Example 3, the Bitcoin Power Law indicates that Bitcoin's price cannot surpass approximately $808K before 2030 as can be seen at point E, while also ensuring it will be at least $224K by then (point F).
WaveTrend, RSI, VWAP, MFI Indicator🚀 Strategy Overview
This multi-confirmation indicator combines WaveTrend, RSI, VWAP, and MFI to generate high-probability buy and sell signals with built-in risk management. It is designed for precision trading across stocks, forex, crypto, and indices.
📊 Entry Conditions
🔹 Long Entry (Buy Signal) 🟢
✅ WaveTrend crosses above the oversold level (-53)
✅ RSI is above 30 (confirming momentum shift)
✅ Price is above VWAP (bullish trend)
✅ MFI is below 20, indicating accumulation
🔹 Short Entry (Sell Signal) 🔴
✅ WaveTrend crosses below the overbought level (+53)
✅ RSI is below 70 (indicating weakness)
✅ Price is below VWAP (bearish trend)
✅ MFI is above 80, indicating distribution
📉 Exit Conditions
🔹 Take Profit (TP) 🎯
💰 Dynamic TP levels based on:
✔ ATR multiplier
✔ Percentage-based profit target
✔ Pip-based TP (for forex traders)
🔹 Stop Loss (SL) 🛑
🔸 ATR-based SL for dynamic risk management
🔸 Percentage-based SL for fixed risk strategies
🔸 Pip-based SL for forex traders
📌 Additional Features
✅ Trend Filter – Ensures trades align with market momentum
✅ ADX Confirmation – Avoids low-volatility setups
✅ Time-Based Filters – Avoids trading outside key market hours
✅ Customizable Settings – Fully adaptable to different assets & strategies
🚀 Maximize your profits with this powerful, multi-confirmation strategy! 🚀
Adaptive KDJ (MTF)Hey guys,
this is an adaptive MTF KDJ oscillator.
Pick up to 3 different timeframes, choose a weighting if you want and enjoy the beautiful signals it will show you.
The length of every timeframe is adaptive and based of the timeframe's ATR.
The plot shows the smoothed average of the 3 KDJ values.
Large triangles show KDJ crossings.
Small triangles show anticipations of possible crossings.
I found out it works best with 1m, 5m, 15m and weighting=1 for forex scalping in 1m.
Use other indicators for confluence.
Bitcoin Power Law OscillatorThis is the oscillator version of the script. The main body of the script can be found here .
Firstly, we would like to give credit to @apsk32 and @x_X_77_X_x as part of the code originates from their work. Additionally, @apsk32 is widely credited with applying the Power Law concept to Bitcoin and popularizing this model within the crypto community. Additionally, the visual layout is fully inspired by @apsk32's designs, and we think it looks amazing. So much so that we had to turn it into a TradingView script. Thank you!
Understanding the Bitcoin Power Law Model
Also called the Long-Term Bitcoin Power Law Model. The Bitcoin Power Law model tries to capture and predict Bitcoin's price growth over time. It assumes that Bitcoin's price follows an exponential growth pattern, where the price increases over time according to a mathematical relationship.
By fitting a power law to historical data, the model creates a trend line that represents this growth. It then generates additional parallel lines (support and resistance lines) to show potential price boundaries, helping to visualize where Bitcoin’s price could move within certain ranges.
In simple terms, the model helps us understand Bitcoin's general growth trajectory and provides a framework to visualize how its price could behave over the long term.
The Bitcoin Power Law has the following function:
Power Law = 10^(a + b * log10(d))
Consisting of the following parameters:
a: Power Law Intercept (default: -17.668).
b: Power Law Slope (default: 5.926).
d: Number of days since a reference point(calculated by counting bars from the reference point with an offset).
Explanation of the a and b parameters:
Roughly explained, the optimal values for the a and b parameters are determined through a process of linear regression on a log-log scale (after applying a logarithmic transformation to both the x and y axes). On this log-log scale, the power law relationship becomes linear, making it possible to apply linear regression. The best fit for the regression is then evaluated using metrics like the R-squared value, residual error analysis, and visual inspection. This process can be quite complex and is beyond the scope of this post.
Applying vertical shifts to generate the other lines:
Once the initial power-law is created, additional lines are generated by applying a vertical shift . This shift is achieved by adding a specific number of days (or years in case of this script) to the d-parameter. This creates new lines perfectly parallel to the initial power law with an added vertical shift, maintaining the same slope and intercept.
In the case of this script, shifts are made by adding +365 days, +2 * 365 days, +3 * 365 days, +4 * 365 days, and +5 * 365 days, effectively introducing one to five years of shifts. This results in a total of six Power Law lines, as outlined below (From lowest to highest):
Base Power Law Line (no shift)
1-year shifted line
2-year shifted line
3-year shifted line
4-year shifted line
5-year shifted line
The six power law lines:
Bitcoin Power Law Oscillator
This publication also includes the oscillator version of the Bitcoin Power Law. This version applies a logarithmic transformation to the price, Base Power Law Line, and 5-year shifted line using the formula log10(x) .
The log-transformed price is then normalized using min-max normalization relative to the log-transformed Base Power Law Line and 5-year shifted line with the formula:
normalized price = log(close) - log(Base Power Law Line) / log(5-year shifted line) - log(Base Power Law Line)
Finally, the normalized price was multiplied by 5 to map its value between 0 and 5, aligning with the shifted lines.
Interpretation of the Bitcoin Power Law Model:
The shifted Power Law lines provide a framework for predicting Bitcoin's future price movements based on historical trends. These lines are created by applying a vertical shift to the initial Power Law line, with each shifted line representing a future time frame (e.g., 1 year, 2 years, 3 years, etc.).
By analyzing these shifted lines, users can make predictions about minimum price levels at specific future dates. For example, the 5-year shifted line will act as the main support level for Bitcoin’s price in 5 years, meaning that Bitcoin’s price should not fall below this line, ensuring that Bitcoin will be valued at least at this level by that time. Similarly, the 2-year shifted line will serve as the support line for Bitcoin's price in 2 years, establishing that the price should not drop below this line within that time frame.
On the other hand, the 5-year shifted line also functions as an absolute resistance , meaning Bitcoin's price will not exceed this line prior to the 5-year mark. This provides a prediction that Bitcoin cannot reach certain price levels before a specific date. For example, the price of Bitcoin is unlikely to reach $100,000 before 2021, and it will not exceed this price before the 5-year shifted line becomes relevant. After 2028, however, the price is predicted to never fall below $100,000, thanks to the support established by the shifted lines.
In essence, the shifted Power Law lines offer a way to predict both the minimum price levels that Bitcoin will hit by certain dates and the earliest dates by which certain price points will be reached. These lines help frame Bitcoin's potential future price range, offering insight into long-term price behavior and providing a guide for investors and analysts. Lets examine some examples:
Example 1:
In Example 1 it can be seen that point A on the 5-year shifted line acts as major resistance . Also it can be seen that 5 years later this price level now corresponds to the Base Power Law Line and acts as a major support (Note: Vertical yearly grid lines have been added for this purpose👍).
Example 2:
In Example 2, the price level at point C on the 3-year shifted line becomes a major support three years later at point C, now aligning with the Base Power Law Line.
Finally, let's explore some future price predictions, as this script provides projections on the weekly timeframe :
Example 3:
In Example 3, the Bitcoin Power Law indicates that Bitcoin's price cannot surpass approximately $808K before 2030 as can be seen at point E, while also ensuring it will be at least $224K by then (point F).
Volume Weighted RSI (VW RSI)The Volume Weighted RSI (VW RSI) is a momentum oscillator designed for TradingView, implemented in Pine Script v6, that enhances the traditional Relative Strength Index (RSI) by incorporating trading volume into its calculation. Unlike the standard RSI, which measures the speed and change of price movements based solely on price data, the VW RSI weights its analysis by volume, emphasizing price movements backed by significant trading activity. This makes the VW RSI particularly effective for identifying bullish or bearish momentum, overbought/oversold conditions, and potential trend reversals in markets where volume plays a critical role, such as stocks, forex, and cryptocurrencies.
Key Features
Volume-Weighted Momentum Calculation:
The VW RSI calculates momentum by comparing the volume associated with upward price movements (up-volume) to the volume associated with downward price movements (down-volume).
Up-volume is the volume on bars where the closing price is higher than the previous close, while down-volume is the volume on bars where the closing price is lower than the previous close.
These volumes are smoothed over a user-defined period (default: 14 bars) using a Running Moving Average (RMA), and the VW RSI is computed using the formula:
\text{VW RSI} = 100 - \frac{100}{1 + \text{VoRS}}
where
\text{VoRS} = \frac{\text{Average Up-Volume}}{\text{Average Down-Volume}}
.
Oscillator Range and Interpretation:
The VW RSI oscillates between 0 and 100, with a centerline at 50.
Above 50: Indicates bullish volume momentum, suggesting that volume on up bars dominates, which may signal buying pressure and a potential uptrend.
Below 50: Indicates bearish volume momentum, suggesting that volume on down bars dominates, which may signal selling pressure and a potential downtrend.
Overbought/Oversold Levels: User-defined thresholds (default: 70 for overbought, 30 for oversold) help identify potential reversal points:
VW RSI > 70: Overbought, indicating a possible pullback or reversal.
VW RSI < 30: Oversold, indicating a possible bounce or reversal.
Visual Elements:
VW RSI Line: Plotted in a separate pane below the price chart, colored dynamically based on its value:
Green when above 50 (bullish momentum).
Red when below 50 (bearish momentum).
Gray when at 50 (neutral).
Centerline: A dashed line at 50, optionally displayed, serving as the neutral threshold between bullish and bearish momentum.
Overbought/Oversold Lines: Dashed lines at the user-defined overbought (default: 70) and oversold (default: 30) levels, optionally displayed, to highlight extreme conditions.
Background Coloring: The background of the VW RSI pane is shaded red when the indicator is in overbought territory and green when in oversold territory, providing a quick visual cue of potential reversal zones.
Alerts:
Built-in alerts for key events:
Bullish Momentum: Triggered when the VW RSI crosses above 50, indicating a shift to bullish volume momentum.
Bearish Momentum: Triggered when the VW RSI crosses below 50, indicating a shift to bearish volume momentum.
Overbought Condition: Triggered when the VW RSI crosses above the overbought threshold (default: 70), signaling a potential pullback.
Oversold Condition: Triggered when the VW RSI crosses below the oversold threshold (default: 30), signaling a potential bounce.
Input Parameters
VW RSI Length (default: 14): The period over which the up-volume and down-volume are smoothed to calculate the VW RSI. A longer period results in smoother signals, while a shorter period increases sensitivity.
Overbought Level (default: 70): The threshold above which the VW RSI is considered overbought, indicating a potential reversal or pullback.
Oversold Level (default: 30): The threshold below which the VW RSI is considered oversold, indicating a potential reversal or bounce.
Show Centerline (default: true): Toggles the display of the 50 centerline, which separates bullish and bearish momentum zones.
Show Overbought/Oversold Lines (default: true): Toggles the display of the overbought and oversold threshold lines.
How It Works
Volume Classification:
For each bar, the indicator determines whether the price movement is upward or downward:
If the current close is higher than the previous close, the bar’s volume is classified as up-volume.
If the current close is lower than the previous close, the bar’s volume is classified as down-volume.
If the close is unchanged, both up-volume and down-volume are set to 0 for that bar.
Smoothing:
The up-volume and down-volume are smoothed using a Running Moving Average (RMA) over the specified period (default: 14 bars) to reduce noise and provide a more stable measure of volume momentum.
VW RSI Calculation:
The Volume Relative Strength (VoRS) is calculated as the ratio of smoothed up-volume to smoothed down-volume.
The VW RSI is then computed using the standard RSI formula, but with volume data instead of price changes, resulting in a value between 0 and 100.
Visualization and Alerts:
The VW RSI is plotted with dynamic coloring to reflect its momentum direction, and optional lines are drawn for the centerline and overbought/oversold levels.
Background coloring highlights overbought and oversold conditions, and alerts notify the trader of significant crossings.
Usage
Timeframe: The VW RSI can be used on any timeframe, but it is particularly effective on intraday charts (e.g., 1-hour, 4-hour) or daily charts where volume data is reliable. Shorter timeframes may require a shorter length for increased sensitivity, while longer timeframes may benefit from a longer length for smoother signals.
Markets: Best suited for markets with significant and reliable volume data, such as stocks, forex, and cryptocurrencies. It may be less effective in markets with low or inconsistent volume, such as certain futures contracts.
Trading Strategies:
Trend Confirmation:
Use the VW RSI to confirm the direction of a trend. For example, in an uptrend, look for the VW RSI to remain above 50, indicating sustained bullish volume momentum, and consider buying on pullbacks when the VW RSI dips but stays above 50.
In a downtrend, look for the VW RSI to remain below 50, indicating sustained bearish volume momentum, and consider selling on rallies when the VW RSI rises but stays below 50.
Overbought/Oversold Conditions:
When the VW RSI crosses above 70, the market may be overbought, suggesting a potential pullback or reversal. Consider taking profits on long positions or preparing for a short entry, but confirm with price action or other indicators.
When the VW RSI crosses below 30, the market may be oversold, suggesting a potential bounce or reversal. Consider entering long positions or covering shorts, but confirm with additional signals.
Divergences:
Look for divergences between the VW RSI and price to spot potential reversals. For example, if the price makes a higher high but the VW RSI makes a lower high, this bearish divergence may signal an impending downtrend.
Conversely, if the price makes a lower low but the VW RSI makes a higher low, this bullish divergence may signal an impending uptrend.
Momentum Shifts:
A crossover above 50 can signal the start of bullish momentum, making it a potential entry point for long trades.
A crossunder below 50 can signal the start of bearish momentum, making it a potential entry point for short trades or an exit for long positions.
Example
On a 4-hour SOLUSDT chart:
During an uptrend, the VW RSI might rise above 50 and stay there, confirming bullish volume momentum. If it approaches 70, it may indicate overbought conditions, as seen near a price peak of 145.08, suggesting a potential pullback.
During a downtrend, the VW RSI might fall below 50, confirming bearish volume momentum. If it drops below 30 near a price low of 141.82, it may indicate oversold conditions, suggesting a potential bounce, as seen in a slight recovery afterward.
A bullish divergence might occur if the price makes a lower low during the downtrend, but the VW RSI makes a higher low, signaling a potential reversal.
Limitations
Lagging Nature: Like the traditional RSI, the VW RSI is a lagging indicator because it relies on smoothed data (RMA). It may not react quickly to sudden price reversals, potentially missing the start of new trends.
False Signals in Ranging Markets: In choppy or ranging markets, the VW RSI may oscillate around 50, generating frequent crossovers that lead to false signals. Combining it with a trend filter (e.g., ADX) can help mitigate this.
Volume Data Dependency: The VW RSI relies on accurate volume data, which may be inconsistent or unavailable in some markets (e.g., certain forex pairs or futures contracts). In such cases, the indicator’s effectiveness may be reduced.
Overbought/Oversold in Strong Trends: During strong trends, the VW RSI can remain in overbought or oversold territory for extended periods, leading to premature exit signals. Use additional confirmation to avoid exiting too early.
Potential Improvements
Smoothing Options: Add options to use different smoothing methods (e.g., EMA, SMA) instead of RMA for the up/down volume calculations, allowing users to adjust the indicator’s responsiveness.
Divergence Detection: Include logic to detect and plot bullish/bearish divergences between the VW RSI and price, providing visual cues for potential reversals.
Customizable Colors: Allow users to customize the colors of the VW RSI line, centerline, overbought/oversold lines, and background shading.
Trend Filter: Integrate a trend strength filter (e.g., ADX > 25) to ensure signals are generated only during strong trends, reducing false signals in ranging markets.
The Volume Weighted RSI (VW RSI) is a powerful tool for traders seeking to incorporate volume into their momentum analysis, offering a unique perspective on market dynamics by emphasizing price movements backed by significant trading activity. It is best used in conjunction with other indicators and price action analysis to confirm signals and improve trading decisions.
Advanced SMC + Oscillator + RSI + Trend Analysis IndicatorAdvanced SMC + Oscillator + RSI + Trend Analysis Indicator
This TradingView indicator integrates multiple technical analysis tools to help traders make informed decisions. It combines Smart Money Concept (SMC), Moving Averages, RSI, MACD, Bollinger Bands, VWAP, and Trend Analysis to provide a comprehensive view of market conditions.
Features & Functions
1. Moving Averages (SMA & EMA)
Simple Moving Average (SMA): A standard moving average that smooths price action over a defined period (default 14).
Exponential Moving Average (EMA): A faster-moving average (default 50) that reacts more quickly to price changes.
Usage: Price crossing above the SMA or EMA can indicate bullish momentum, while crossing below suggests bearish momentum.
2. Buy & Sell Signals
Buy Signal:
Occurs when price crosses above the SMA.
RSI is below 30 (oversold condition).
MACD line crosses above the Signal line (bullish momentum).
Sell Signal:
Occurs when price crosses below the SMA.
RSI is above 70 (overbought condition).
MACD line crosses below the Signal line (bearish momentum).
Plotting:
Buy signals appear as green upward arrows below the bars.
Sell signals appear as red downward arrows above the bars.
3. Smart Money Concept (SMC) - Market Structure
Identifies higher highs (HH) and lower lows (LL) over the last 20 bars.
Bullish Structure: Price moves above the highest high, indicating possible trend continuation.
Bearish Structure: Price moves below the lowest low, signaling potential bearish trends.
Plotting:
Bullish Structure: Blue triangles appear below the bars.
Bearish Structure: Orange triangles appear above the bars.
4. MACD (Moving Average Convergence Divergence)
MACD Line & Signal Line: Used to gauge momentum shifts.
MACD Histogram: Displays the difference between MACD Line and Signal Line.
Usage:
Positive histogram values indicate bullish momentum.
Negative values indicate bearish momentum.
Plotting:
MACD Histogram is shown in purple.
5. RSI (Relative Strength Index)
Measures the strength of recent price movements (default 14 period).
Overbought (>70): Price may be due for a pullback.
Oversold (<30): Price may be ready for a reversal.
Plotting:
RSI line is orange.
Overbought and oversold levels are marked with red (70) and green (30) lines.
6. Bollinger Bands (BB)
Upper Band: Shows potential overbought levels.
Lower Band: Shows potential oversold levels.
Usage:
Price touching the upper band suggests overbought conditions.
Price touching the lower band suggests oversold conditions.
Plotting:
Bollinger Bands are shown in gray.
7. Volume Weighted Average Price (VWAP)
VWAP is a key indicator used by institutional traders to determine the average trading price weighted by volume.
Usage:
Price above VWAP signals bullish strength.
Price below VWAP signals bearish weakness.
Plotting:
VWAP line is shown in purple.
8. Trend Direction Indicators
Uptrend:
Price is above the EMA.
MACD is bullish (MACD line above Signal line).
Displayed as blue triangles below bars.
Downtrend:
Price is below the EMA.
MACD is bearish (MACD line below Signal line).
Displayed as red triangles above bars.
How to Use This Indicator?
Confirm Trends:
Look at the EMA and SMA crossover.
Identify bullish or bearish structures (HH/LL).
Observe trend indicators (blue/red triangles).
Check Momentum & Reversals:
Look at the MACD Histogram and crossover.
Check RSI for overbought/oversold conditions.
Monitor Volatility:
Use Bollinger Bands to gauge extreme price movements.
Observe VWAP for institutional trading levels.
Trigger Trades:
Enter long trades when buy signals appear (price above SMA, RSI <30, MACD bullish).
Enter short trades when sell signals appear (price below SMA, RSI >70, MACD bearish).
Conclusion
This indicator is a powerful multi-tool that provides trend direction, momentum shifts, volatility analysis, and precise buy/sell signals. It is useful for traders who want a complete technical analysis system in one chart.
Custom DeMarker🔹 What does this indicator do?
✅ It shows the DeMarker line.
✅ It gives a buy signal below 0.30, a sell signal above 0.70.
✅ It shows signals with arrows on the chart.
Overextension Oscillator [by MR_LUCAS_01]The Overextension Oscillator is a custom TradingView indicator designed to detect market overextension by analyzing swing highs/lows, price distance from swings, and momentum shifts. It helps traders identify potential reversal zones where price is likely to retrace after an overextended move.
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Key Features:
✅ Swing High/Low Detection – Identifies key pivot points to measure market extension.
✅ Overextension Calculation – Measures how far price has moved from recent swings.
✅ Oscillator with Smoothing – Uses EMA-based smoothing to generate reliable signals.
✅ Buy & Sell Signals – Highlights potential entry points when price reaches extreme zones.
✅ Color-Coded Candle Plotting – Enhances visibility of bullish and bearish zones.
✅ Threshold-Based Alerts – Notifies traders when price extends beyond a calculated limit.
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How It Works:
1. Swing Point Detection:
Detects pivot highs/lows over a defined number of bars.
Measures the percentage difference between recent swing highs and lows.
2. Overextension Calculation:
Determines how far price has moved from the last significant swing.
Compares this movement to historical averages to assess overextension.
3. Oscillator Processing:
Creates an oscillator with smoothing to filter noise.
Highlights when price moves too far from equilibrium.
4. Buy & Sell Signal Generation:
Buy Signal: Price falls below a threshold relative to the last swing low.
Sell Signal: Price rises above a threshold relative to the last swing high.
Includes a cooldown period to avoid rapid repeated signals.
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Visualization:
Bullish & Bearish Candles:
Green candles indicate bullish overextensions (potential reversals up).
Red candles indicate bearish overextensions (potential reversals down).
Signal Line (Neutral Color):
Plots a smoothed oscillator to track overextension movements.
Reference Levels:
0 Line: Center equilibrium.
+20 & -20 Bands: Indicate potential reversal zones.
Buy & Sell Signal Markers:
Green circles (Buy Signals) at oversold conditions.
Red circles (Sell Signals) at overbought conditions.
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Customization Options:
Swing Detection Lengths – Adjust bar lookback periods for pivots.
Oscillator Smoothing – Modify EMA lengths for signal clarity.
Threshold Sensitivity – Fine-tune overextension criteria.
Cooldown Period – Prevent excessive signals in rapid moves.
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Use Cases:
✔ Reversal Traders – Identify overextended price action for mean reversion trades.
✔ Trend Traders – Use as a filter to confirm pullback entries in trends.
✔ Scalpers & Swing Traders – Find quick reversals in volatile markets.
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Conclusion:
The Overextension Oscillator is a powerful tool that helps traders identify extreme price movements and anticipate potential reversals. By combining swing analysis, distance measurements, and smoothed oscillations, it provides clear buy/sell signals in overextended markets.
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.
