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.
Oscillatori
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.
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.
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 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
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.
NUPL Z-Score | Vistula LabsWhat is NUPL?
NUPL (Net Unrealized Profit/Loss) is a fundamental on-chain metric used to evaluate the profit or loss state of a cryptocurrency's market participants, such as Bitcoin (BTC) and Ethereum (ETH). It compares the current market capitalization—the total value of all coins at their current price—to the realized capitalization, which represents the average price at which all coins were last transacted on-chain.
Market Capitalization: Current price × circulating supply.
Realized Capitalization: The sum of the value of all coins based on the price at their last on-chain movement.
For Bitcoin (BTC):
NUPL = (Market Cap - Realized Cap) / Market Cap * 100
For Ethereum (ETH):
NUPL = (Market Cap - Realized Cap) / Market Cap
A positive NUPL indicates that the market holds unrealized profits, meaning the current value exceeds the price at which coins were last moved. A negative NUPL signals unrealized losses. Extreme NUPL values—high positives or low negatives—can suggest overvaluation (potential market tops) or undervaluation (potential market bottoms), respectively.
How NUPL is Calculated for BTC & ETH
This indicator calculates NUPL using data sourced from Glassnode and CoinMetrics:
For Bitcoin:
Market Cap: GLASSNODE:BTC_MARKETCAP
Realized Cap: COINMETRICS:BTC_MARKETCAPREAL
Formula: ((btc_market_cap - btc_market_cap_real) / btc_market_cap) * 100
For Ethereum:
Market Cap: GLASSNODE:ETH_MARKETCAP
Realized Cap: COINMETRICS:ETH_MARKETCAPREAL
Formula: ((eth_market_cap - eth_market_cap_real) / eth_market_cap) * 100
The indicator then transforms these NUPL values into a Z-Score, which measures how many standard deviations the current NUPL deviates from its historical average. The Z-Score calculation incorporates:
A customizable moving average of NUPL (options: SMA, EMA, DEMA, RMA, WMA, VWMA) over a user-defined length (default: 220 periods).
The standard deviation of NUPL over a specified lookback period (default: 200 periods).
Z-Score Formula:
Z-Score = (Current NUPL - Moving Average of NUPL) / Standard Deviation of NUPL
This normalization allows the indicator to highlight extreme market conditions regardless of the raw NUPL scale.
How This Indicator Can Be Used
Trend Following
The NUPL Z-Score indicator employs a trend-following system with adjustable thresholds to generate trading signals:
Long Signals: Triggered when the Z-Score crosses above the Long Threshold (default: 0.26).
Short Signals: Triggered when the Z-Score crosses below the Short Threshold (default: -0.62).
Visual Representations:
Green up-triangles: Indicate long entry points (plotted below the bar).
Red down-triangles: Indicate short entry points (plotted above the bar).
Color-coded elements:
Candles and Z-Score plot turn teal (#00ffdd) for long positions.
Candles and Z-Score plot turn magenta (#ff00bf) for short positions.
These signals leverage historical NUPL trends to identify potential momentum shifts, aiding traders in timing entries and exits.
Overbought/Oversold Conditions
The indicator flags extreme market states using additional thresholds:
Overbought Threshold (default: 3.0): When the Z-Score exceeds this level, the market may be significantly overvalued, hinting at potential selling pressure. Highlighted with a light magenta background (#ff00bf with 75% transparency).
Oversold Threshold (default: -2.0): When the Z-Score drops below this level, the market may be significantly undervalued, suggesting buying opportunities. Highlighted with a light teal background (#00ffdd with 75% transparency).
These extreme Z-Score levels have historically aligned with major market peaks and troughs, making them useful for medium- to long-term position management.
Customization Options
Traders can tailor the indicator to their preferences:
Cryptocurrency Source: Choose between BTC or ETH.
Moving Average Type: Select from SMA, EMA, DEMA, RMA, WMA, or VWMA.
Moving Average Length: Adjust the period for the NUPL moving average (default: 220).
Z-Score Lookback Period: Set the historical window for Z-Score calculation (default: 200).
Thresholds: Fine-tune values for: Long Threshold (default: 0.26), Short Threshold (default: -0.62), Overbought Threshold (default: 3.0), Oversold Threshold (default: -2.0)
These options enable users to adapt the indicator to various trading strategies and risk profiles.
Alerts
The indicator supports four alert conditions to keep traders informed:
NUPL Long Opportunity: Alerts when a long signal is triggered.
NUPL Short Opportunity: Alerts when a short signal is triggered.
NUPL Overbought Condition: Alerts when the Z-Score exceeds the overbought threshold.
NUPL Oversold Condition: Alerts when the Z-Score falls below the oversold threshold.
These alerts allow traders to monitor key opportunities without constantly watching the chart.
leo.usdt RSIThis is a custom RSI-based indicator enhanced with Bollinger Band logic to give early visual signals and potential trading alerts. It’s designed to help traders identify:
• Overbought/oversold conditions
• Potential reversals
• Areas of market neutrality or retracement
• Strong or weak trade signal zones
• RSI-based alerts in combination with price volatility (via Bollinger Bands)
⸻
Key Components:
1. RSI Calculation
• Standard Relative Strength Index (RSI) with a default length of 21.
• Source: close price.
• Output RSI is color-coded:
• Red if RSI > 62 (overbought)
• Blue if RSI < 24 (oversold)
• Green otherwise
2. RSI Zones and Visual Labels
• Horizontal lines mark 6 key RSI zones:
• 80 = Upper Bound (extreme overbought)
• 62 = Strong Sell
• 50 = Possible Retrace
• 38 = Neutral
• 24 = Possible Reversal
• 16 = Strong Buy
• Backgrounds between these zones are color-shaded to visually differentiate them.
• Optional labels (controlled via checkbox) appear on the RSI chart to show zone names.
3. Bollinger Bands Logic
• Bollinger Bands use:
• Length = 20 (default)
• Multiplier = 2.0 (default)
• The BB logic categorizes price position relative to bands:
• BB = 71: Price above upper band
• BB = 56: Price in upper half
• BB = 44: Price in middle
• BB = 31: Price in lower half
• BB = 20: Price below lower band
4. Combined RSI + BB Signal Circles
• Two types of visual circles appear on the RSI chart:
• Small Signal (line width = 2):
• RSI > 50 and price > BB upper band → small sell signal (red)
• RSI < 38 and price < BB lower band → small buy signal (blue)
• Strong Signal (line width = 4):
• RSI > 62 and price > BB upper band → strong sell
• RSI < 24 and price < BB lower band → strong buy
5. Alerts
This script includes 4 alert conditions:
• Small Sell: RSI > 50 and price above BB upper band
• Small Buy: RSI < 38 and price below BB lower band
• Strong Sell: RSI > 62 and price above BB upper band
• Strong Buy: RSI < 24 and price below BB lower band
⸻
User Controls:
• Change RSI length.
• Change Bollinger Band length and multiplier.
• Toggle signals (circles) on/off.
• Toggle RSI zone labels on/off.
• Optional RSI zone background shading for easy visualization.
⸻
Use Case:
This script is ideal for:
• Traders looking to combine momentum (RSI) with volatility (BB) for better signal accuracy.
• Spotting potential trend reversals, retracement zones, and extreme conditions.
• Getting visual and audible alerts to act faster during key moments.
Momentum Based RSIThe Momentum Based RSI is an enhancement to the RSI. it incorporates 2 sections:
MA Ratio (Fast/Slow)
RSI
at the end both of those are multiplied to create a more responsive RSI which reacts fast to market moves while still providing a whip ressistant tool.
Momentum Calculation
The "MA Ratio" as i like to call it results from comparing 2 MAs (both can be set to whatever type you like) against eachother, which, in the end, provides a Ratio that visualizes the difference. It is simple yet effective
RSI
An Old yet popular tool which dates back to 1978. In and out of itself it is a great tool, however it still can be enhanced.
The Combination
The RSI and the MARatio are multiplied together, which results in an RSI that is ampliefied by the speed of the market movements.
This proves highly effective, since the MA Ratio is hovering around at the same level. However during trends, it picks up speed in either of both directions which marginally increases the RSI's response the said movement.
Why its Creative, New and Good
While it is a super simple concept, it still holds a lot of power relative to its sophistication. Traders may use it like they used the Vanilla RSI (e.g Trend following, Mean-reversion or other).
Unlike RSI with momentum overlays, this indicator actively uses an MA Ratio multiplier for simplicity and responsiveness.
At last, Its primary goal is to detect trends faster while not creating more noise & false signals.
What not to do
if youre using this indicator, please do NOT change the Fast MA to be slower than to Slow MA or vice versa, since you'll be getting broken & noise induced signals which may not align with your goals.
Great inventions require great Care
As with anything, you should not use this tool without any other confluence. As great as the backtests may be, you dont know what the future holds, be careful!
This indicator is not a guaranteed predicition tool. If youre going to use it for investment decisions, please use it in coherence with other tools.
Thank you for reading!
ADX BoxDescription:
The ADX Box indicator provides traders with a quick and intuitive way to monitor the current trend strength based on the Average Directional Index (ADX), calculated with a customisable period (default: 7 periods).
This compact indicator neatly displays the current ADX value rounded to one decimal place, along with a clear directional arrow:
Green upward triangle (▲): Indicates that ADX is rising above its moving average, signaling increasing trend strength.
Red downward triangle (▼): Indicates that ADX is declining below its moving average, signaling weakening trend strength.
Key Features:
Small and clean visual representation.
Dynamically updates in real-time directly on the chart.
Ideal for quick trend strength assessment without cluttering your workspace.
Recommended Usage:
Quickly identifying whether market trends are strengthening or weakening.
Enhancing decision-making for trend-following or breakout trading strategies.
Complementing other indicators such as ATR boxes for volatility measurement.
Feel free to use, share, and incorporate this indicator into your trading setups for clearer insights and more confident trading decisions!
Multi-Fibonacci Trend Average[FibonacciFlux]Multi-Fibonacci Trend Average (MFTA): An Institutional-Grade Trend Confluence Indicator for Discerning Market Participants
My original indicator/Strategy:
Engineered for the sophisticated demands of institutional and advanced traders, the Multi-Fibonacci Trend Average (MFTA) indicator represents a paradigm shift in technical analysis. This meticulously crafted tool is designed to furnish high-definition trend signals within the complexities of modern financial markets. Anchored in the rigorous principles of Fibonacci ratios and augmented by advanced averaging methodologies, MFTA delivers a granular perspective on trend dynamics. Its integration of Multi-Timeframe (MTF) filters provides unparalleled signal robustness, empowering strategic decision-making with a heightened degree of confidence.
MFTA indicator on BTCUSDT 15min chart with 1min RSI and MACD filters enabled. Note the refined signal generation with reduced noise.
MFTA indicator on BTCUSDT 15min chart without MTF filters. While capturing more potential trading opportunities, it also generates a higher frequency of signals, including potential false positives.
Core Innovation: Proprietary Fibonacci-Enhanced Supertrend Averaging Engine
The MFTA indicator’s core innovation lies in its proprietary implementation of Supertrend analysis, strategically fortified by Fibonacci ratios to construct a truly dynamic volatility envelope. Departing from conventional Supertrend methodologies, MFTA autonomously computes not one, but three distinct Supertrend lines. Each of these lines is uniquely parameterized by a specific Fibonacci factor: 0.618 (Weak), 1.618 (Medium/Golden Ratio), and 2.618 (Strong/Extended Fibonacci).
// Fibonacci-based factors for multiple Supertrend calculations
factor1 = input.float(0.618, 'Factor 1 (Weak/Fibonacci)', minval=0.01, step=0.01, tooltip='Factor 1 (Weak/Fibonacci)', group="Fibonacci Supertrend")
factor2 = input.float(1.618, 'Factor 2 (Medium/Golden Ratio)', minval=0.01, step=0.01, tooltip='Factor 2 (Medium/Golden Ratio)', group="Fibonacci Supertrend")
factor3 = input.float(2.618, 'Factor 3 (Strong/Extended Fib)', minval=0.01, step=0.01, tooltip='Factor 3 (Strong/Extended Fib)', group="Fibonacci Supertrend")
This multi-faceted architecture adeptly captures a spectrum of market volatility sensitivities, ensuring a comprehensive assessment of prevailing conditions. Subsequently, the indicator algorithmically synthesizes these disparate Supertrend lines through arithmetic averaging. To achieve optimal signal fidelity and mitigate inherent market noise, this composite average is further refined utilizing an Exponential Moving Average (EMA).
// Calculate average of the three supertends and a smoothed version
superlength = input.int(21, 'Smoothing Length', tooltip='Smoothing Length for Average Supertrend', group="Fibonacci Supertrend")
average_trend = (supertrend1 + supertrend2 + supertrend3) / 3
smoothed_trend = ta.ema(average_trend, superlength)
The resultant ‘Smoothed Trend’ line emerges as a remarkably responsive yet stable trend demarcation, offering demonstrably superior clarity and precision compared to singular Supertrend implementations, particularly within the turbulent dynamics of high-volatility markets.
Elevated Signal Confluence: Integrated Multi-Timeframe (MTF) Validation Suite
MFTA transcends the limitations of conventional trend indicators by incorporating an advanced suite of three independent MTF filters: RSI, MACD, and Volume. These filters function as sophisticated validation protocols, rigorously ensuring that only signals exhibiting a confluence of high-probability factors are brought to the forefront.
1. Granular Lower Timeframe RSI Momentum Filter
The Relative Strength Index (RSI) filter, computed from a user-defined lower timeframe, furnishes critical momentum-based signal validation. By meticulously monitoring RSI dynamics on an accelerated timeframe, traders gain the capacity to evaluate underlying momentum strength with precision, prior to committing to signal execution on the primary chart timeframe.
// --- Lower Timeframe RSI Filter ---
ltf_rsi_filter_enable = input.bool(false, title="Enable RSI Filter", group="MTF Filters", tooltip="Use RSI from lower timeframe as a filter")
ltf_rsi_timeframe = input.timeframe("1", title="RSI Timeframe", group="MTF Filters", tooltip="Timeframe for RSI calculation")
ltf_rsi_length = input.int(14, title="RSI Length", minval=1, group="MTF Filters", tooltip="Length for RSI calculation")
ltf_rsi_threshold = input.int(30, title="RSI Threshold", minval=0, maxval=100, group="MTF Filters", tooltip="RSI value threshold for filtering signals")
2. Convergent Lower Timeframe MACD Trend-Momentum Filter
The Moving Average Convergence Divergence (MACD) filter, also calculated on a lower timeframe basis, introduces a critical layer of trend-momentum convergence confirmation. The bullish signal configuration rigorously mandates that the MACD line be definitively positioned above the Signal line on the designated lower timeframe. This stringent condition ensures a robust indication of converging momentum that aligns synergistically with the prevailing trend identified on the primary timeframe.
// --- Lower Timeframe MACD Filter ---
ltf_macd_filter_enable = input.bool(false, title="Enable MACD Filter", group="MTF Filters", tooltip="Use MACD from lower timeframe as a filter")
ltf_macd_timeframe = input.timeframe("1", title="MACD Timeframe", group="MTF Filters", tooltip="Timeframe for MACD calculation")
ltf_macd_fast_length = input.int(12, title="MACD Fast Length", minval=1, group="MTF Filters", tooltip="Fast EMA length for MACD")
ltf_macd_slow_length = input.int(26, title="MACD Slow Length", minval=1, group="MTF Filters", tooltip="Slow EMA length for MACD")
ltf_macd_signal_length = input.int(9, title="MACD Signal Length", minval=1, group="MTF Filters", tooltip="Signal SMA length for MACD")
3. Definitive Volume Confirmation Filter
The Volume Filter functions as an indispensable arbiter of trade conviction. By establishing a dynamic volume threshold, defined as a percentage relative to the average volume over a user-specified lookback period, traders can effectively ensure that all generated signals are rigorously validated by demonstrably increased trading activity. This pivotal validation step signifies robust market participation, substantially diminishing the potential for spurious or false breakout signals.
// --- Volume Filter ---
volume_filter_enable = input.bool(false, title="Enable Volume Filter", group="MTF Filters", tooltip="Use volume level as a filter")
volume_threshold_percent = input.int(title="Volume Threshold (%)", defval=150, minval=100, group="MTF Filters", tooltip="Minimum volume percentage compared to average volume to allow signal (100% = average)")
These meticulously engineered filters operate in synergistic confluence, requiring all enabled filters to definitively satisfy their pre-defined conditions before a Buy or Sell signal is generated. This stringent multi-layered validation process drastically minimizes the incidence of false positive signals, thereby significantly enhancing entry precision and overall signal reliability.
Intuitive Visual Architecture & Actionable Intelligence
MFTA provides a demonstrably intuitive and visually rich charting environment, meticulously delineating trend direction and momentum through precisely color-coded plots:
Average Supertrend: Thin line, green/red for uptrend/downtrend, immediate directional bias.
Smoothed Supertrend: Bold line, teal/purple for uptrend/downtrend, cleaner, institutionally robust trend.
Dynamic Trend Fill: Green/red fill between Supertrends quantifies trend strength and momentum.
Adaptive Background Coloring: Light green/red background mirrors Smoothed Supertrend direction, holistic trend perspective.
Precision Buy/Sell Signals: ‘BUY’/‘SELL’ labels appear on chart when trend touch and MTF filter confluence are satisfied, facilitating high-conviction trade action.
MFTA indicator applied to BTCUSDT 4-hour chart, showcasing its effectiveness on higher timeframes. The Smoothed Length parameter is increased to 200 for enhanced smoothness on this timeframe, coupled with 1min RSI and Volume filters for signal refinement. This illustrates the indicator's adaptability across different timeframes and market conditions.
Strategic Applications for Institutional Mandates
MFTA’s sophisticated design provides distinct advantages for advanced trading operations and institutional investment mandates. Key strategic applications include:
High-Probability Trend Identification: Fibonacci-averaged Supertrend with MTF filters robustly identifies high-probability trend continuations and reversals, enhancing alpha generation.
Precision Entry/Exit Signals: Volume and momentum-filtered signals enable institutional-grade precision for optimized risk-adjusted returns.
Algorithmic Trading Integration: Clear signal logic facilitates seamless integration into automated trading systems for scalable strategy deployment.
Multi-Asset/Timeframe Versatility: Adaptable parameters ensure applicability across diverse asset classes and timeframes, catering to varied trading mandates.
Enhanced Risk Management: Superior signal fidelity from MTF filters inherently reduces false signals, supporting robust risk management protocols.
Granular Customization and Parameterized Control
MFTA offers unparalleled customization, empowering users to fine-tune parameters for precise alignment with specific trading styles and market conditions. Key adjustable parameters include:
Fibonacci Factors: Adjust Supertrend sensitivity to volatility regimes.
ATR Length: Control volatility responsiveness in Supertrend calculations.
Smoothing Length: Refine Smoothed Trend line responsiveness and noise reduction.
MTF Filter Parameters: Independently configure timeframes, lookback periods, and thresholds for RSI, MACD, and Volume filters for optimal signal filtering.
Disclaimer
MFTA is meticulously engineered for high-quality trend signals; however, no indicator guarantees profit. Market conditions are unpredictable, and trading involves substantial risk. Rigorous backtesting and forward testing across diverse datasets, alongside a comprehensive understanding of the indicator's logic, are essential before live deployment. Past performance is not indicative of future results. MFTA is for informational and analytical purposes only and is not financial or investment advice.
Trend Magnet ProTrend Magnet Pro – Advanced Adaptive Trend & Oscillator Indicator
Overview:
Trend Magnet Pro is a powerful, fully customizable indicator that combines adaptive moving averages with a dynamic oscillator to provide a comprehensive view of market trends and potential reversal points. It integrates multiple analytical layers—volatility, volume, multi-timeframe analysis, and divergence detection—to help traders make informed decisions.
Key Features & Competitive Advantages:
Adaptive Moving Average (MA):
The indicator calculates an adaptive MA by blending your chosen MA type (SMA, EMA, WMA, VWMA, KAMA, or LSMA) with Kaufman’s Adaptive Moving Average (KAMA). This hybrid approach adjusts dynamically to market volatility, ensuring smoother trend detection and reducing noise during erratic periods.
Custom Oscillator Calculation:
A separate oscillator is computed based on the difference between the closing price and a dedicated oscillator MA. This difference is normalized using an ATR-based volatility measure and then smoothed with the Hull MA. This process enhances signal precision by filtering out minor fluctuations.
ATR & Volume Integration:
Using the Average True Range (ATR) for volatility and a volume spike detection mechanism, the indicator filters out weak signals. These features ensure that only significant market moves trigger trading signals.
Multi-Timeframe Analysis:
By incorporating an oscillator analysis on a higher timeframe, Trend Magnet Pro provides an extra layer of confirmation. This multi-timeframe approach improves the reliability of signals, making it easier to identify sustained trends.
Divergence Detection:
The indicator automatically detects bullish and bearish divergences between price movements and the oscillator. These divergences can serve as early warnings for potential trend reversals, adding further depth to your market analysis.
Visual Clarity & Customization:
Trend Magnet Pro offers:
A separate oscillator panel with color-coded histograms.
Overlay plots of the adaptive MA on the price chart.
Clear visual markers for buy and sell signals.
Adjustable parameters for pivot detection and oscillator pressure thresholds.
How to Use Trend Magnet Pro:
Main MA Settings:
Choose your preferred MA type and set the MA length for the main trend analysis.
The adaptive algorithm will blend this with KAMA based on current volatility.
Oscillator Settings:
Set the oscillator’s MA type and its smoothing length.
Fine-tune the oscillator parameters to match your trading style and market conditions.
Common Settings:
Define the ATR length for volatility measurement.
Adjust the volume multiplier and volume SMA period to enable volume spike detection.
Set the low and high pressure thresholds to determine oscillator color changes, reflecting different market pressures.
Multi-Timeframe & Divergence:
Optionally, select a higher timeframe for the oscillator to provide additional confirmation.
Enable divergence detection to highlight potential trend reversals based on price and oscillator pivots.
Signal Interpretation:
Buy Signal: Triggered when the oscillator crosses above zero, accompanied by volume spikes and confirmed by both multi-timeframe analysis and price being above the adaptive MA.
Sell Signal: Triggered under opposite conditions, where the oscillator crosses below zero and the price is below the adaptive MA.
By adjusting these settings, you can tailor Trend Magnet Pro to your specific market and trading strategy, making it an invaluable tool for both trend-following and reversal trading.
Русское Описание
Trend Magnet Pro – Индикатор Адаптивного Тренда и Осциллятора
Обзор:
Trend Magnet Pro – это мощный и полностью настраиваемый индикатор, который объединяет адаптивные скользящие средние с динамическим осциллятором для комплексного анализа рыночных трендов и потенциальных точек разворота. Он интегрирует несколько аналитических слоёв — волатильность, объём, мультитаймфреймовый анализ и детекцию дивергенций — что позволяет принимать обоснованные торговые решения.
Основные преимущества и функциональные возможности:
Адаптивная Скользящая Средняя (MA):
Индикатор рассчитывает адаптивную MA, комбинируя выбранный тип (SMA, EMA, WMA, VWMA, KAMA или LSMA) с Kaufman’s Adaptive Moving Average (KAMA). Такой гибридный подход динамически подстраивается под рыночную волатильность, обеспечивая более плавное определение трендов и снижая уровень шума в нестабильные периоды.
Кастомизированный Расчёт Осциллятора:
Осциллятор вычисляется отдельно на основе разницы между ценой закрытия и специально рассчитанной MA для осциллятора. Эта разница нормализуется с использованием ATR (Average True Range) для оценки волатильности и сглаживается при помощи Hull MA, что позволяет точнее фиксировать значимые сигналы и исключать мелкие колебания.
Интеграция ATR и Объёма:
Применение ATR для измерения волатильности в сочетании с механизмом обнаружения всплесков объёма позволяет отсеивать слабые сигналы. Эти функции гарантируют, что торговые сигналы возникают только при значительных движениях рынка.
Мультитаймфреймовый Анализ:
Встроенный анализ осциллятора на старшем таймфрейме даёт дополнительное подтверждение сигналов. Такой подход повышает надёжность сигналов, помогая выявлять устойчивые тренды.
Детекция Дивергенций:
Индикатор автоматически обнаруживает бычьи и медвежьи дивергенции между движением цены и осциллятором. Эти дивергенции могут служить ранним предупреждением о потенциальном развороте тренда, что добавляет глубины вашему анализу.
Удобство Визуализации и Настройки:
Trend Magnet Pro предлагает:
Отдельную панель осциллятора с цветными гистограммами.
Наложение адаптивной MA на график цены.
Чёткие визуальные сигналы для покупки и продажи.
Настраиваемые параметры для обнаружения пивотов и уровней давления осциллятора.
Как работать с Trend Magnet Pro:
Настройки Основной MA:
Выберите предпочитаемый тип MA и установите период для анализа основного тренда.
Адаптивный алгоритм объединит выбранную MA с KAMA на основе текущей волатильности.
Настройки Осциллятора:
Задайте тип MA для осциллятора и установите период сглаживания.
Подберите параметры осциллятора, чтобы он соответствовал вашему стилю торговли и рыночным условиям.
Общие Настройки:
Определите период ATR для измерения волатильности.
Настройте множитель объёма и период SMA объёма для обнаружения всплесков.
Установите пороги низкого и высокого давления, которые будут влиять на цветовую индикацию осциллятора и отражать рыночное давление.
Мультитаймфреймовый Анализ и Дивергенции:
При необходимости выберите старший таймфрейм для осциллятора, чтобы обеспечить дополнительное подтверждение сигналов.
Включите функцию детекции дивергенций для выявления потенциальных разворотов тренда на основе пивотов цены и осциллятора.
Интерпретация Сигналов:
Сигнал на покупку: Формируется, когда осциллятор пересекает ноль снизу вверх, подтверждаясь всплеском объёма, анализом на старшем таймфрейме и положением цены выше адаптивной MA.
Сигнал на продажу: Формируется при обратных условиях – когда осциллятор пересекает ноль сверху вниз, а цена находится ниже адаптивной MA.
Настройка параметров позволяет адаптировать Trend Magnet Pro под конкретный рынок и торговую стратегию, делая его незаменимым инструментом как для трендового анализа, так и для поиска разворотных сигналов.
Aggregated Spot vs Perp Volume (% Change)Aggregated Spot vs Perp Volume (% Change)
Description
The "Aggregated Spot vs Perp Volume (% Change)" indicator helps crypto traders compare the momentum of spot and perpetual futures (perp) trading volumes across 12 major exchanges. It calculates the percentage change in volume from one bar to the next, highlighting divergences and showing which market—spot or perp—is leading a move. By focusing on relative changes, it eliminates the issue of absolute volume differences, making trends clear.
The indicator aggregates data from Binance, Bybit, OKX, Coinbase, Bitget, MEXC, Phemex, BingX, WhiteBIT, BitMEX, Kraken, and HTX. Users can toggle exchanges and choose to measure volume in coin units (e.g., BTC) or USD.
How It Works
Volume Aggregation:
Fetches spot and perp volume data for the selected crypto (e.g., BTC) from up to 12 exchanges.
Spot volume is included only if perp volume is available for the same pair, ensuring consistency.
Volume can be measured in coin units or USD (volume × spot price).
Percentage Change:
Calculates the percentage change in spot and perp volumes from the previous bar:
Percentage Change = ((Current Volume − Previous Volume) / Previous Volume) ×100
This focuses on relative momentum, making spot and perp volumes directly comparable.
Visualization:
Spot volume % change is plotted as a blue line, and perp volume % change as a red line, both with a linewidth of 1.
Who Should Use It
Crypto Traders: To understand spot vs. perp market dynamics across exchanges.
Momentum Traders: To spot which market is driving price moves via volume divergences.
Scalpers/Day Traders: For identifying short-term shifts in market activity.
Analysts: To study liquidity and sentiment in crypto markets.
How to Use It
Blue line: Spot volume % change.
Red line: Perp volume % change.
Look for divergences (e.g., a sharp rise in the red line but not the blue line suggests perp markets are leading).
Combine with Price:
Use alongside price charts to confirm trends or spot potential reversals.
Context
Spot markets reflect actual asset trading, while perp markets, with leverage, attract speculative activity and often show higher volumes. This indicator uses percentage change to compare their momentum, helping traders identify market leadership and divergences. For example, a 50% increase in both spot and perp volumes plots at the same level, making it easy to see relative shifts across exchanges.
Stochastic Fusion Elite [trade_lexx]📈 Stochastic Fusion Elite is your reliable trading assistant!
📊 What is Stochastic Fusion Elite ?
Stochastic Fusion Elite is a trading indicator based on a stochastic oscillator. It analyzes the rate of price change and generates buy or sell signals based on various technical analysis methods.
💡 The main components of the indicator
📊 Stochastic oscillator (K and D)
Stochastic shows the position of the current price relative to the price range for a certain period. Values above 80 indicate overbought (an early sale is possible), and values below 20 indicate oversold (an early purchase is possible).
📈 Moving Averages (MA)
The indicator uses 10 different types of moving averages to smooth stochastic lines.:
- SMA: Simple moving average
- EMA: Exponential moving average
- WMA: Weighted moving average
- HMA: Moving Average Scale
- KAMA: Kaufman Adaptive Moving Average
- VWMA: Volume-weighted moving average
- ALMA: Arnaud Legoux Moving Average
- TEMA: Triple exponential moving average
- ZLEMA: zero delay exponential moving average
- DEMA: Double exponential moving average
The choice of the type of moving average affects the speed of the indicator's response to market changes.
🎯 Bollinger Bands (BB)
Bands around the moving average that widen and narrow depending on volatility. They help determine when the stochastic is out of the normal range.
🔄 Divergences
Divergences show discrepancies between price and stochastic:
- Bullish divergence: price is falling and stochastic is rising — an upward reversal is possible
- Bearish divergence: the price is rising, and stochastic is falling — a downward reversal is possible
🔍 Indicator signals
1️⃣ KD signals (K and D stochastic lines)
- Buy signal:
- What happens: the %K line crosses the %D line from bottom to top
- What does it look like: a green triangle with the label "KD" under the chart and the label "Buy" below the bar
- What does this mean: the price is gaining an upward momentum, growth is possible
- Sell signal:
- What happens: the %K line crosses the %D line from top to bottom
- What it looks like: a red triangle with the label "KD" above the chart and the label "Sell" above the bar
- What does this mean: the price is losing its upward momentum, possibly falling
2️⃣ Moving Average Signals (MA)
- Buy Signal:
- What happens: stochastic crosses the moving average from bottom to top
- What it looks like: a green triangle with the label "MA" under the chart and the label "Buy" below the bar
- What does this mean: stochastic is starting to accelerate upward, price growth is possible
- Sell signal:
- What happens: stochastic crosses the moving average from top to bottom
- What it looks like: a red triangle with the label "MA" above the chart and the label "Sell" above the bar
- What does this mean: stochastic is starting to accelerate downwards, a price drop is possible
3️⃣ Bollinger Band Signals (BB)
- Buy signal:
- What happens: stochastic crosses the lower Bollinger band from bottom to top
- What it looks like: a green triangle with the label "BB" under the chart and the label "Buy" below the bar
- What does this mean: stochastic was too low and is now starting to recover
- Sell signal:
- What happens: Stochastic crosses the upper Bollinger band from top to bottom
- What it looks like: a red triangle with a "BB" label above the chart and a "Sell" label above the bar
- What does this mean: stochastic was too high and is now starting to decline
4️⃣ Divergence Signals (Div)
- Buy Signal (Bullish Divergence):
- What's happening: the price is falling, and stochastic is forming higher lows
- What it looks like: a green triangle with a "Div" label under the chart and a "Buy" label below the bar
- What does this mean: despite the falling price, the momentum is already changing in an upward direction
- Sell signal (bearish divergence):
- What's going on: the price is rising, and stochastic is forming lower highs
- What it looks like: a red triangle with a "Div" label above the chart and a "Sell" label above the bar
- What does this mean: despite the price increase, the momentum is already weakening
🛠️ Filters to filter out false signals
1️⃣ Minimum distance between the signals
- What it does: sets the minimum number of candles between signals
- Why it is needed: prevents signals from being too frequent during strong market fluctuations
- How to set it up: Set the number from 0 and above (default: 5)
2️⃣ "Waiting for the opposite signal" mode
- What it does: waits for a signal in the opposite direction before generating a new signal
- Why you need it: it helps you not to miss important trend reversals
- How to set up: just turn the function on or off
3️⃣ Filter by stochastic levels
- What it does: generates signals only when the stochastic is in the specified ranges
- Why it is needed: it helps to catch the moments when the market is oversold or overbought
- How to set up:
- For buy signals: set a range for oversold (for example, 1-20)
- For sell signals: set a range for overbought (for example, 80-100)
4️⃣ MFI filter
- What it does: additionally checks the values of the cash flow index (MFI)
- Why it is needed: confirms stochastic signals with cash flow data
- How to set it up:
- For buy signals: set the range for oversold MFI (for example, 1-25)
- For sell signals: set the range for overbought MFI (for example, 75-100)
5️⃣ The RSI filter
- What it does: additionally checks the RSI values to confirm the signals
- Why it is needed: adds additional confirmation from another popular indicator
- How to set up:
- For buy signals: set the range for oversold MFI (for example, 1-30)
- For sell signals: set the range for overbought MFI (for example, 70-100)
🔄 Signal combination modes
1️⃣ Normal mode
- How it works: all signals (KD, MA, BB, Div) work independently of each other
- When to use it: for general market analysis or when learning how to work with the indicator
2️⃣ "AND" Mode ("AND Mode")
- How it works: the alarm appears only when several conditions are triggered simultaneously
- Combination options:
- KD+MA: signals from the KD and moving average lines
- KD+BB: signals from KD lines and Bollinger bands
- KD+Div: signals from the KD and divergence lines
- KD+MA+BB: three signals simultaneously
- KD+MA+Div: three signals at the same time
- KD+BB+Div: three signals at the same time
- KD+MA+BB+Div: all four signals at the same time
- When to use: for more reliable but rare signals
🔌 Connecting to trading strategies
The indicator can be connected to your trading strategies using 6 different channels.:
1. Connector KD signals: connects only the signals from the intersection of lines K and D
2. Connector MA signals: connects only signals from moving averages
3. Connector BB signal: connects only the signals from the Bollinger bands
4. Connector divergence signals: connects only divergence signals
5. Combined Connector: connects any signals
6. Connector for "And" mode: connects only combined signals
🔔 Setting up alerts
The indicator can send alerts when alarms appear.:
- Alerts for KD: when the %K line crosses the %D line
- Alerts for MA: when stochastic crosses the moving average
- Alerts for BB: when stochastic crosses the Bollinger bands
- Divergence alerts: when a divergence is detected
- Combined alerts: for all types of alarms
- Alerts for "And" mode: for combined signals
🎭 What does the indicator look like on the chart ?
- Main lines K and D: blue and orange lines
- Overbought/oversold levels: horizontal lines at levels 20 and 80
- Middle line: dotted line at level 50
- Stochastic Moving Average: yellow line
- Bollinger bands: green lines around the moving average
- Signals: green and red triangles with corresponding labels
📚 How to start using Stochastic Fusion Elite
1️⃣ Initial setup
- Add an indicator to your chart
- Select the types of signals you want to use (KD, MA, BB, Div)
- Adjust the period and smoothing for the K and D lines
2️⃣ Filter settings
- Set the distance between the signals to get rid of unnecessary noise
- Adjust stochastic, MFI and RSI levels depending on the volatility of your asset
- If you need more reliable signals, turn on the "Waiting for the opposite signal" mode.
3️⃣ Operation mode selection
- First, use the standard mode to see all possible signals.
- When you get comfortable, try the "And" mode for rarer signals.
4️⃣ Setting up Alerts
- Select the types of signals you want to be notified about
- Set up alerts for these types of signals
5️⃣ Verification and adaptation
- Check the operation of the indicator on historical data
- Adjust the parameters for a specific asset
- Adapt the settings to your trading style
🌟 Usage examples
For trend trading
- Use the KD and MA signals in the direction of the main trend
- Set the distance between the signals
- Set stricter levels for filters
For trading in a sideways range
- Use BB signals to detect bounces from the range boundaries
- Use a stochastic level filter to confirm overbought/oversold conditions
- Adjust the Bollinger bands according to the width of the range
To determine the pivot points
- Pay attention to the divergence signals
- Set the distance between the signals
- Check the MFI and RSI filters for additional confirmation
Quantum Motion Oscillator-QMO (TechnoBlooms)Quantum Motion Oscillator (QMO) is a momentum indicator designed for traders who demand precision. Combining multi-timeframe weighted linear regression with EMA crossovers, QMO offers a dynamic view of market momentum, helping traders anticipate trend shifts with greater accuracy.
This oscillator is inspired by quantum mechanics and wave theory, where market movement is seen as a series of probabilistic waves rather than rigid structures.
The histogram is plotted in proportion to the price movement of the candlesticks.
KEY FEATURES
1. Multi-Timeframe Histogram - Integrates 1 to 5 weighted linear regression averages, reducing lag while maintaining accuracy.
2. EMA Crossover Signal - Uses a Short and Long EMA to confirm trend shifts with minimal noise.
3. Adaptive Trend Analysis - Self-adjusting mechanics make QMO effective in both ranging and trending markets.
4. Scalable for Different Trading Styles - Works seamlessly for scalping, intraday, swing and position trading.
ADVANCED PROFESSIONAL INSIGHTS
1. Wave Dynamics and Market Flow - Inspired by wave mechanics, QMO reflects the energy accumulation and dissipation in price movements.
Expanding histogram waves = Strong momentum surge
Contracting waves = Momentum weakening, potential reversal zone.
2. Liquidity and Order Flow Applications - QMO works well alongside liquidity concepts and smart money techniques:
Combine with Fair Value Gaps & Order Blocks -> Enter when QMO signals align with liquidity zones.
Avoid False Moves - If price sweeps liquidity, but QMO momentum diverges, it is a sign of potential smart money manipulation.
BBTrend ModularBBTrend Modular Indicator
The BBTrend Modular Indicator is designed to provide multiple perspectives on market dynamics by leveraging Bollinger Bands. It consists of several independent modules, each analyzing a specific aspect of price action and volatility. By default, all modules are enabled except the Adaptive BBTrend module, which remains off unless activated.
Modules Overview
1. BBTrend (Basic Module)
- Purpose: Calculates the difference between the absolute differences of the upper and lower Bollinger Bands for short and long periods.
- Working Principle: By comparing the divergence between the bands of two different timeframes, this module gives an indication of trend strength. A positive value may indicate bullish conditions, while a negative value suggests bearish conditions.
2. BB Expansion (Volatility Analysis)
- Purpose: Measures market volatility by comparing the widths of the Bollinger Bands for short and long periods.
- Working Principle: An increased width in the short-period bands relative to the long-period bands signals rising volatility and trend expansion. Dynamic color coding further helps in interpreting volatility changes.
3. BB Momentum Trend (Slope Analysis)
- Purpose: Assesses market momentum by analyzing the slope (or the rate of change) of the short-period Bollinger Band’s middle line.
- Working Principle: A positive slope indicates upward (bullish) momentum, whereas a negative slope points to downward (bearish) momentum. This helps traders gauge the speed at which the trend is evolving.
4. Adaptive BBTrend (Adaptive Calculation)
- Purpose: Adjusts the Bollinger Bands’ lengths dynamically using the Average True Range (ATR) to better respond to changing market volatility.
- Working Principle: An adaptive factor is computed based on the ATR relative to the closing price. This factor recalculates the short and long periods, making the indicator more responsive in volatile markets. Note: This module is disabled by default.
5. Volume-Weighted BBTrend (Volume Adjustment)
- Purpose: Integrates trading volume into the BBTrend calculation to refine the trend signal.
- Working Principle: By applying a volume weighting factor, the module gives more significance to high-volume bars. This results in a nuanced view of market sentiment, where high trading activity can strengthen the trend signal.
Русское описание
Индикатор BBTrend Modular
Индикатор BBTrend Modular создан для предоставления различных взглядов на динамику рынка с использованием полос Боллинджера. Он состоит из нескольких независимых модулей, каждый из которых анализирует определенный аспект ценового действия и волатильности. По умолчанию все модули включены, за исключением модуля Adaptive BBTrend, который остается выключенным, если его специально не активировать.
Обзор модулей
1. BBTrend (Базовый модуль)
- Назначение: Рассчитывает разницу между абсолютными разностями верхней и нижней линий полос Боллинджера для короткого и длинного периодов.
- Принцип работы: Сравнивая дивергенцию полос для двух разных временных периодов, модуль предоставляет информацию о силе тренда. Положительное значение может свидетельствовать о бычьем тренде, а отрицательное — о медвежьем.
2. BB Expansion (Анализ волатильности)
- Назначение: Оценивает волатильность рынка путем сравнения ширины полос Боллинджера для короткого и длинного периодов.
- Принцип работы: Увеличение ширины короткого периода по сравнению с длинным сигнализирует об увеличении волатильности и расширении тренда. Динамическая раскраска помогает визуально интерпретировать изменения волатильности.
3. BB Momentum Trend (Анализ наклона)
- Назначение: Анализирует моментум рынка путем оценки наклона (темпа изменения) средней линии полос Боллинджера для короткого периода.
- Принцип работы: Положительный наклон указывает на бычий моментум, а отрицательный — на медвежий, что помогает оценить скорость изменения тренда.
4. Adaptive BBTrend (Адаптивный расчет)
- Назначение: Динамически корректирует периоды расчета полос Боллинджера с использованием Average True Range (ATR) для лучшей адаптации к изменяющейся волатильности рынка.
- Принцип работы: Вычисляется адаптивный коэффициент на основе ATR относительно цены закрытия, после чего пересчитываются короткий и длинный периоды. Такой подход позволяет индикатору быть более чувствительным в условиях высокой волатильности. Примечание: Модуль по умолчанию выключен.
5. Volume-Weighted BBTrend (Объёмно-взвешенный расчет)
- Назначение: Включает объем торгов в расчет BBTrend для уточнения сигнала тренда.
- Принцип работы: Модуль применяет объемное взвешивание, придавая больший вес барам с высоким объемом. Это позволяет получить более точное представление о настроениях рынка, когда высокий объем торговли усиливает сигнал тренда.
RSI-Colored Price Candles with BackgroundThis Pine Script indicator visually enhances price candles based on **RSI (Relative Strength Index)** behavior, helping traders quickly assess momentum directly on the price chart.
**RSI Calculation:**
The RSI is computed using a traditional 14-period lookback. It uses `ta.rma()` to smooth average gains and losses, and then transforms the result into an RSI value between 0 and 100. This value is used to determine both **candle color** and optional **background shading**.
**Candle Coloring:**
Each price candle is recolored based on the current RSI value:
- If RSI is **greater than or equal to 50**, the candle is **bright green**, indicating bullish momentum.
- If RSI is **less than 50**, the candle is **bright red**, indicating bearish momentum.
The actual OHLC values of the candles remain unchanged. Only their color is modified to reflect RSI strength.
**Optional Background Highlighting:**
A user setting called `Show Overbought/Oversold Background` lets traders toggle background shading on or off. When enabled:
- If RSI is **above 70**, a soft **green** background appears, signaling overbought conditions.
- If RSI is **below 30**, a soft **red** background appears, signaling oversold conditions.
This provides an intuitive visual cue that highlights potential reversal or exhaustion zones based on RSI extremes.
**Custom Settings:**
- The RSI length and source are customizable.
- Background highlighting is turned **off by default**, giving users a clean chart unless they choose to enable it.
**Purpose and Use:**
This script is designed for traders who want to visually integrate RSI momentum directly into their chart candles, reducing the need to look away from price action. It's clean, responsive, and adjustable — perfect for intraday or swing traders who value simplicity backed by momentum data.
Melody Markets RSI UT+🎵 Melody Markets RSI UT+ – The Reinvented RSI Indicator 🎵
📌 Indicator Description
The Melody Markets RSI UT+ takes the Relative Strength Index (RSI) to the next level by incorporating acceleration zones and the ability to display RSI levels from higher timeframes directly on the price chart.
Unlike traditional RSI indicators that appear in a separate window, Melody Markets RSI UT+ transforms RSI bands into dynamic levels around the price, making market analysis more intuitive and responsive.
🔍 Key Features
✅ RSI integrated into the main chart → No need for a separate window, the RSI is displayed as bands around the price.
✅ RSI Acceleration Zones → Detects bullish and bearish acceleration movements, highlighting price momentum.
✅ Multi-TF RSI Display → View RSI levels from higher timeframes (5min, 15min, 1H, 4H, D...) without switching charts.
✅ Automatic RSI Breakout Detection → Highlights reintegrations and breakouts of RSI boundaries to anticipate reversals.
✅ Fully Customizable → Enable/disable RSI levels, adjust colors and styles of RSI zones, and fine-tune indicator sensitivity.
✅ Built-in Alerts → Get notified when the price crosses a key RSI level or reintegrates an acceleration zone.
📖 How to Use Melody Markets RSI UT+?
1️⃣ Enable RSI Zones → Display RSI bands directly around the price to spot strength or weakness areas.
2️⃣ Identify RSI Accelerations → Colored zones highlight explosive bullish or bearish movements.
3️⃣ Track Higher TF RSI Levels → Enable RSI levels from higher timeframes (H1, H4, D...) to identify key support and resistance zones.
4️⃣ Use RSI Alerts → Get instant notifications when a breakout or reintegration of an RSI level occurs.
5️⃣ Refine Your Entries & Exits → Combine this indicator with other technical tools to confirm your trading decisions.
🎯 Who Is This Indicator For?
🔹 Scalpers & Day Traders → Huge time-saver with an RSI directly integrated into the main chart.
🔹 Swing Traders → Easily track key RSI levels without constantly switching timeframes.
🔹 Beginners & Advanced Traders → A more intuitive and fluid RSI reading, perfect for anticipating market moves.
🔥 Conclusion
Melody Markets RSI UT+ is a game-changing indicator that redefines how RSI is used in trading. Its seamless integration into the main chart, combined with higher TF visualization and acceleration zones, allows traders to make sharper decisions with unmatched clarity.
📌 Add it to your chart now and unlock a new way to trade RSI! 🚀
Touch HMA + ATR Band Bands Alert (NTY88)🔔 Precision Alerts | No Repainting | ATR-Based Touch Detection | HMA Trend Coloring
This script is a clean and powerful tool designed to help you catch precise market reversals using ATR Band touches combined with trend-following logic.
📌 How It Works
A custom Hull Moving Average (HMA) is used to track the trend.
Two dynamic ATR-based bands are drawn above and below the HMA.
A signal is generated when the closing price touches the upper or lower ATR band within a small tolerance zone.
✅ Key Features
🔁 Alternating Signals: Only one Buy → then one Sell → then Buy again. No signal spam.
🟢🔴 Color-Changing HMA Line: Green = HMA rising | Red = HMA falling
📏 Price Tolerance Input: Define how close the candle must be to the ATR band to trigger a signal.
🔔 Real-Time Alerts: Easily set alerts for Buy and Sell signals — works in live markets.
🚫 No Repainting: All signals are confirmed at candle close and will not change afterward.
🎯 When to Use
Great for trend reversals, scalping zones, or identifying potential exhaustion points.
Works well on any timeframe or market (crypto, stocks, forex).
💬 Pro Tip:
Combine this with RSI, Volume, or ADX filters to build a complete confluence system.
📈 Built for traders who love clean logic, precision entries, and visual clarity.
Highest High Line with Multi-Timeframe Supertrend and RSIOverview:
This powerful indicator combines three essential elements for traders:
Highest High Line – Tracks the highest price over a customizable lookback period across different timeframes.
Multi-Timeframe Supertrend – Displays Supertrend values and trend directions for multiple timeframes simultaneously.
Relative Strength Index (RSI) – Shows RSI values across different timeframes for momentum analysis.
Features:
✅ Customizable Highest High Line:
Selectable timeframes: Daily, Weekly, Monthly, Quarterly, Yearly
Adjustable lookback period
✅ Multi-Timeframe Supertrend:
Supports 1min, 5min, 10min, 15min, 30min, 1H, Daily, Weekly, Monthly, Quarterly, Yearly
ATR-based calculation with configurable ATR period and multiplier
Identifies bullish (green) & bearish (red) trends
✅ Multi-Timeframe RSI:
Calculates RSI for the same timeframes as Supertrend
Overbought (≥70) and Oversold (≤30) signals with color coding
✅ Comprehensive Table Display:
A clean, structured table in the bottom-right corner
Displays Supertrend direction, value, and RSI for all timeframes
Helps traders quickly assess trend and momentum alignment
How to Use:
Use the Highest High Line to identify key resistance zones.
Confirm trend direction with Multi-Timeframe Supertrend.
Check RSI values to avoid overbought/oversold conditions before entering trades.
Align multiple timeframes for stronger confirmation of trend shifts.
Ideal For:
✅ Scalpers (lower timeframes: 1m–30m)
✅ Swing Traders (higher timeframes: 1H–D)
✅ Position Traders (Weekly, Monthly, Quarterly)
💡 Tip: Look for Supertrend & RSI confluence across multiple timeframes for higher probability setups.
Stochastic and MACD HistogramStochastic-MACD Fusion Histogram (concept)
How It Works:
This indicator combines Stochastic Oscillator and MACD Histogram to create a unique momentum-tracking histogram. It blends stochastic-based overbought/oversold levels with MACD-based trend strength, helping traders identify potential reversals and trend momentum more effectively.
Stochastic Component: Measures where the price is relative to its recent range, highlighting overbought/oversold conditions.
MACD Component: Captures momentum shifts by calculating the difference between two EMAs and a signal line.
Fusion Algorithm: The MACD histogram is normalized and combined with the Stochastic %K using a weighted formula (60% Stoch, 40% MACD) to smooth fluctuations and improve signal clarity.
Usage:
Histogram Colors:
Blue / SkyBlue: Positive momentum increasing.
Red / LightRed: Negative momentum increasing.
Levels:
Overbought (>30): Potential selling pressure.
Oversold (<-30): Potential buying pressure.
Zero Line: Momentum shift zone.
Notes:
Best to combine it with others indicators for trend confirmation, like Moving Average, MACD, etc.
This indicator is good for quick entry/exit in futures market, from few seconds up to minutes.
It works well on 5 minutes candle. Regular Hours works better.
To sell wait for histogram to go OVER overbought level, once the first candle reach BELOW the overbought level hit sell. Same strategy for buy when it hits oversold level. Make sure you won't use the indicator alone.
ADR Checker - Breakouts📈 ADR Checker – Breakouts
Gain the edge by knowing when a stock has already made its move.
🚀 What It Does:
The ADR Checker - Breakouts is a powerful yet simple visual tool that helps traders instantly assess whether a stock has already exceeded its Average Daily Range (ADR) for the day — a critical piece of information for momentum traders, swing traders, and especially those following breakout, VCP, or CANSLIM strategies.
Using a customizable on-screen table that always stays in view (regardless of zoom or chart scaling), this script shows:
✅ Average ADR% – 20-day average range, calculated in %.
📊 Today’s Move – how much the stock has moved today.
🔥 % of Avg ADR – today's move relative to its historical average, with live color feedback:
🟥 Over 100% (Overextended – danger!)
🟧 70-100% (Caution zone)
🟩 Below 70% (Room to move)
💡 Why It Matters:
One of the most overlooked mistakes by breakout traders is entering a trade after the move has already happened. If a stock has already moved more than its typical daily range, the odds of further continuation sharply decrease, while the risk of pullback or chop increases.
With this tool, you can:
🚫 Avoid chasing extended breakouts
🎯 Time entries before the real move
⚠️ Quickly assess risk/reward potential intraday
🧠 Example Use Case:
Imagine you're watching a classic VCP setup or flat base breakout. The stock breaks out on volume—but when you check this indicator, you see:
Today’s Move: 7.2%
Avg ADR: 5.3%
% of ADR: 135% 🟥
This tells you the stock is already well beyond its average daily range. While it may continue higher, odds now favor a consolidation, shakeout, or pullback. This is your cue to wait for a better entry or pass entirely.
On the flip side, if the breakout just started and the % of ADR is still under 50%, you have confirmation that there’s room to run — giving you more confidence to enter early.
⚙️ Fully Customizable:
Choose position on screen (top/bottom left/right)
Customize text color, background, and size
🔧 Install This Tool and:
✅ Stop chasing extended moves
✅ Add discipline to your entries
✅ Improve your breakout win rate
Perfect for VCP, CANSLIM, and BREAKOUT traders who want a clean, edge-enhancing visual guide.
TMO (True Momentum Oscillator)TMO ((T)rue (M)omentum (O)scilator)
Created by Mobius V01.05.2018 TOS Convert to TV using Claude 3.7 and ChatGPT 03 Mini :
TMO calculates momentum using the delta of price. Giving a much better picture of trend, tend reversals and divergence than momentum oscillators using price.
True Momentum Oscillator (TMO)
The True Momentum Oscillator (TMO) is a momentum-based technical indicator designed to identify trend direction, trend strength, and potential reversal points in the market. It's particularly useful for spotting overbought and oversold conditions, aiding traders in timing their entries and exits.
How it Works:
The TMO calculates market momentum by analyzing recent price action:
Momentum Calculation:
For a user-defined length (e.g., 14 bars), TMO compares the current closing price to past open prices. It assigns:
+1 if the current close is greater than the open price of the past bar (indicating bullish momentum).
-1 if it's less (indicating bearish momentum).
0 if there's no change.
The sum of these scores gives a raw momentum measure.
EMA Smoothing:
To reduce noise and false signals, this raw momentum is smoothed using Exponential Moving Averages (EMAs):
First, the raw data is smoothed by an EMA over a short calculation period (default: 5).
Then, it undergoes additional smoothing through another EMA (default: 3 bars), creating the primary "Main" line of the indicator.
Lastly, a "Signal" line is derived by applying another EMA (also default: 3 bars) to the main line, adding further refinement.
Trend Identification:
The indicator plots two lines:
Main Line: Indicates current momentum strength and direction.
Signal Line: Acts as a reference line, similar to a moving average crossover system.
When the Main line crosses above the Signal line, it suggests strengthening bullish momentum. Conversely, when the Main line crosses below the Signal line, it indicates increasing bearish momentum.
Overbought/Oversold Levels:
The indicator identifies key levels based on the chosen length parameter:
Overbought zone (positive threshold): Suggests the market might be overheated, and a potential bearish reversal or pullback could occur.
Oversold zone (negative threshold): Suggests the market might be excessively bearish, signaling a potential bullish reversal.
Clouds visually mark these overbought/oversold areas, making it easy to see potential reversal zones.
Trading Applications:
Trend-following: Traders can enter positions based on crossovers of the Main and Signal lines.
Reversals: The overbought and oversold areas highlight high-probability reversal points.
Momentum confirmation: Use TMO to confirm price action or other technical signals, improving trade accuracy and timing.
The True Momentum Oscillator provides clarity in identifying momentum shifts, making it a valuable addition to various trading strategies.
