OCM UTXO Whale DistributionOCM UTXO Whale Distribution
🔓 This is a premium indicator. This script detects potential large-scale profit-taking by long-term Bitcoin holders — particularly "whales" — using UTXO (Unspent Transaction Output) data. It analyzes surges in the average value of spent outputs (USD-denominated), smoothed over multiple timeframes to isolate significant deviations.
Combined with price action and volatility filters, the tool flags potential whale exit events: moments when smart money may be distributing into strength. These high-conviction signals are marked directly on the chart, helping traders align with hidden on-chain behaviour that rarely shows up in price alone.
⏰ Timeframes to be used on:
Daily
Btc!
OCM Realized Cap AccelerationOCM Realized Cap Acceleration
🔓 This is a premium indicator. It monitors the momentum of Bitcoin’s realized cap on a smoothed, daily basis, offering a nuanced view of capital flow dynamics across the network. By analyzing the rate of change in realized value per coin — normalized by circulating supply — it provides an early signal of market sentiment shifts that are not always visible through price alone.
The resulting acceleration metric is color-coded to reflect subtle gradations in bullish or bearish pressure. It serves as a macro-aware oscillator for long-term positioning or for gauging systemic stress and euphoria in the broader Bitcoin market structure.
⏰ Timeframes to be used on:
Daily
Weekly
Ultimate T3 Fibonacci for BTC Scalping. Look at backtest report!Hey Everyone!
I created another script to add to my growing library of strategies and indicators that I use for automated crypto trading! This strategy is for BITCOIN on the 30 minute chart since I designed it to be a scalping strategy. I calculated for trading fees, and use a small amount of capital in the backtest report. But feel free to modify the capital and how much per order to see how it changes the results:)
It is called the "Ultimate T3 Fibonacci Indicator by NHBprod" that computes and displays two T3-based moving averages derived from price data. The t3_function calculates the Tilson T3 indicator by applying a series of exponential moving averages to a combined price metric and then blending these results with specific coefficients derived from an input factor.
The script accepts several user inputs that toggle the use of the T3 filter, select the buy signal method, and set parameters like lengths and volume factors for two variations of the T3 calculation. Two T3 lines, T3 and T32, are computed with different parameters, and their colors change dynamically (green/red for T3 and blue/purple for T32) based on whether the lines are trending upward or downward. Depending on the selected signal method, the script generates buy signals either when T32 crosses over T3 or when the closing price is above T3, and similarly, sell signals are generated on the respective conditions for crossing under or closing below. Finally, the indicator plots the T3 lines on the chart, adds visual buy/sell markers, and sets alert conditions to notify users when the respective trading signals occur.
The user has the ability to tune the parameters using TP/SL, date timerames for analyses, and the actual parameters of the T3 function including the buy/sell signal! Lastly, the user has the option of trading this long, short, or both!
Let me know your thoughts and check out the backtest report!
OCM Logarithmic Deviation TorchOCM Logarithmic Deviation Torch
🔓 This is a premium indicator. The Logarithmic Deviation Torch is a dynamic risk signalling overlay that transforms daily logarithmic price divergence into a normalized probabilistic gauge. By anchoring its logic to long-term trend baselines and structurally adjusting for time via a custom power-scaling method, it identifies asymmetrical extremes in market positioning with visual clarity.
Coloured dots are plotted above price action in real time, representing a graduated risk scale that evolves alongside underlying volatility. This tool is ideal for traders seeking a forward-biased perspective on overextension or compression within a logarithmic return environment — without relying on reactivity or lag-prone indicators.
⏰ Timeframes to be used on:
Daily
Weekly
OCM Z-Field MACDOCM Z-Field MACD
The Z-Field MACD is a custom momentum oscillator that leverages a zero-lag exponential framework to identify pressure gradients within price structure. By tracking the deviation of a smoothed midline from dynamic high and low boundaries, it surfaces key inflection points often obscured by traditional MACD signals.
Colour-coded histogram bars provide immediate visual cues on directional bias and intensity, supporting faster response to emerging momentum shifts. This tool is engineered for traders looking to extract actionable edge from refined trend-resonant signals, without overfitting to short-term noise.
⏰ Timeframes to be used on:
Daily
OCM Puell MultipleOCM Puell Multiple
The Puell Multiple is an on-chain valuation tool that contextualizes Bitcoin miner revenue against a long-term average, offering insight into cyclical market extremes. This dynamic metric highlights shifts in miner profitability that often precede major macro inflection points in Bitcoin’s price history.
By normalizing recent revenue activity and layering adaptive visual elements, the indicator offers a clean, time-sensitive representation of market stress or euphoria. It’s designed for investors seeking to complement technical or macro strategies with deeper on-chain signal awareness—without relying on short-term noise.
⏰ Timeframes to be used on:
Daily
OCM Pi Cycle Top IndicatorOCM Pi Cycle Top Indicator
This overlay indicator is a visual implementation of the Pi Cycle Top strategy, a historically effective method for identifying major Bitcoin market cycle tops. The Pi Cycle Top Indicator has historically been effective in picking out the timing of market cycle highs within 3 days.
It tracks the crossover between two key moving averages:
A 111-day simple moving average (SMA)
A 2x multiple of the 350-day SMA
When the 111-day SMA crosses below the doubled 350-day SMA, the indicator flags a potential market top, marking it on the chart above the current price. This has historically aligned closely with previous Bitcoin macro peaks.
The indicator is designed for daily timeframes but allows for custom resolution input, making it flexible for backtesting. It also continuously plots both moving averages so traders can visually monitor the crossover dynamics in real-time.
⏰ Timeframes to be used on:
Daily
Weekly
OCM SOPR Z-ScoreOCM SOPR Z-Score
This indicator measures the Spent Output Profit Ratio (SOPR) for Bitcoin, smoothed as a Z-Score. SOPR is a key on-chain metric used to assess market profit-taking behaviour by comparing the price at which coins were sold to the price at which they were acquired. Values above 1 indicate profits being realized, while values below 1 suggest selling at a loss.
The indicator features a heatmap-style colour gradient reflecting the SOPR value intensity, making it easier to visually identify shifts in market sentiment. Subtle background highlights appear when the SOPR crosses upper or lower threshold levels, configurable to highlight overbought or oversold profit-taking extremes. This tool offers a straightforward way to monitor when the market may be topping or bottoming based on realized profit trends.
⏰ Timeframes to be used on:
Daily
Weekly
OCM Net Unrealized Profit/Loss (NUPL)OCM Net Unrealized Profit/Loss (NUPL)
This indicator tracks Bitcoin’s Net Unrealized Profit/Loss (NUPL), a key on-chain metric that reflects the difference between unrealised profits and losses across all market participants. By mapping this ratio, the indicator highlights prevailing market sentiment and investor psychology, segmented into distinct emotional zones ranging from capitulation to euphoria. Each zone is visually distinguished by dynamic colour coding, offering an intuitive way to gauge whether the market is dominated by fear, hope, or greed.
Thresholds for these sentiment zones are configurable, allowing you to tailor sensitivity to different market environments. The plotted NUPL line, combined with reference levels and background highlights for extreme conditions, provides a comprehensive tool to anticipate potential market turning points.
⏰ Timeframes to be used on:
Daily
Weekly
OCM MVRV Z-ScoreOCM MVRV Z-Score
This indicator visualises the Market-Value-to-Realised-Value (MVRV) ratio, a widely used metric to assess Bitcoin market cycles by comparing the market capitalisation against the realised capitalisation. It offers two modes: a standard ratio and a statistically normalised Z-Score variant, enhancing sensitivity to deviations from historical norms. The script applies a gradient colour scheme that dynamically reflects the MVRV value’s relative position within defined overbought and oversold thresholds, allowing you to easily spot cyclical extremes and potential reversal zones.
Critical top and bottom lines are plotted for reference, including an adjustable neutral line, providing further context to Bitcoin's valuation state. This indicator is designed to help you identify periods of market euphoria or distress, making it a robust tool for timing entries and exits within broader market cycles.
⏰ Timeframes to be used on:
Daily
Weekly
OCM 200D MA HeatmapOCM 200D MA Heatmap
This tool visualises Bitcoin’s percentage deviation from its 200-day simple moving average, a long-term reference often associated with deep value zones and cyclical overheats. Each candle is overlaid with a colour-coded dot, with hues shifting according to fixed deviation thresholds. Cooler colours signal periods of market undervaluation, while warmer tones indicate stretched or euphoric price conditions.
The 200D MA line can be optionally displayed, offering a clean view of the long-term trend. Designed as a macro lens for investors and cycle-focused traders, this heatmap distils complex cycle dynamics into an immediate visual signal.
⏰ Timeframes to be used on:
- Daily
- Weekly
BTC Spot/Perp Price DeltaThe indicator gathers price from 3 btc spot pairs (largest by volume) and 3 btc perp pairs (also largest by volume).
The average Spot and Perp prices are then derived.
The indicator plots the price difference between the Spot average and the Perps average (Spot minus Perps).
Green plot above the zero line means Spot price is higher than the Perp price at a candle close - Contango.
Red plot below the zero line means Spot Price is lower than the Perp price at a candle close - Backwardation.
The orange line is the EMA. Default value is 100 periods. Changeable by User.
Use cases:
1. Perp market is way larger than the Spot market, measured by traded Volume. We may say that the Perps market is more "stable", because it is more liquid. When Spot price deviates a lot from the Perps price, in both positive and negative directions, we may expect a mean reversion.
High Green or Red indicator values = expect price reversion.
2. Helps to observe absorption. If the indicator values are high (in both directions), but the price is barely moving, we can come to a conclusion that the opposite side Limit orders are being deployed to absorb Spot market orders.
Typically, this also indicates mean reversion.
3. You are welcome to use the indicator and perhaps find your own use cases.
Any suggestions on how to improve this indicator are welcome.
Spent Output Profit Ratio Z-Score | Vistula LabsOverview
The Spent Output Profit Ratio (SOPR) Z-Score indicator is a sophisticated tool designed by Vistula Labs to help cryptocurrency traders analyze market sentiment and identify potential trend reversals. It leverages on-chain data from Glassnode to calculate the Spent Output Profit Ratio (SOPR) for Bitcoin and Ethereum, transforming this metric into a Z-Score for easy interpretation.
What is SOPR?
Spent Output Profit Ratio (SOPR) measures the profit ratio of spent outputs (transactions) on the blockchain:
SOPR > 1: Indicates that, on average, coins are being sold at a profit.
SOPR < 1: Suggests that coins are being sold at a loss.
SOPR = 1: Break-even point, often seen as a key psychological level.
SOPR provides insights into holder behavior—whether they are locking in profits or cutting losses—making it a valuable gauge of market sentiment.
How It Works
The indicator applies a Z-Score to the SOPR data to normalize it relative to its historical behavior:
Z-Score = (Smoothed SOPR - Moving Average of Smoothed SOPR) / Standard Deviation of Smoothed SOPR
Smoothed SOPR: A moving average (e.g., WMA) of SOPR over a short period (default: 30 bars) to reduce noise.
Moving Average of Smoothed SOPR: A longer moving average (default: 180 bars) of the smoothed SOPR.
Standard Deviation: Calculated over a lookback period (default: 200 bars).
This Z-Score highlights how extreme the current SOPR is compared to its historical norm, helping traders spot significant deviations.
Key Features
Data Source:
Selectable between BTC and ETH, using daily SOPR data from Glassnode.
Customization:
Moving Average Types: Choose from SMA, EMA, DEMA, RMA, WMA, or VWMA for both smoothing and main averages.
Lengths: Adjust the smoothing period (default: 30) and main moving average length (default: 180).
Z-Score Lookback: Default is 200 bars.
Thresholds: Set levels for long/short signals and overbought/oversold conditions.
Signals:
Long Signal: Triggered when Z-Score crosses above 1.02, suggesting potential upward momentum.
Short Signal: Triggered when Z-Score crosses below -0.66, indicating potential downward momentum.
Overbought/Oversold Conditions:
Overbought: Z-Score > 2.5, signaling potential overvaluation.
Oversold: Z-Score < -2.0, indicating potential undervaluation.
Visualizations:
Z-Score Plot: Teal for long signals, magenta for short signals.
Threshold Lines: Dashed for long/short, solid for overbought/oversold.
Candlestick Coloring: Matches signal colors.
Arrows: Green up-triangles for long entries, red down-triangles for short entries.
Background Colors: Magenta for overbought, teal for oversold.
Alerts:
Conditions for Long Opportunity, Short Opportunity, Overbought, and Oversold.
Usage Guide
Select Cryptocurrency: Choose BTC or ETH.
Adjust Moving Averages: Customize types and lengths for smoothing and main averages.
Set Thresholds: Define Z-Score levels for signals and extreme conditions.
Monitor Signals: Use color changes, arrows, and background highlights to identify opportunities.
Enable Alerts: Stay informed without constant chart watching.
Interpretation
High Z-Score (>1.02): SOPR is significantly above its historical mean, potentially indicating overvaluation or strong bullish momentum.
Low Z-Score (<-0.66): SOPR is below its mean, suggesting undervaluation or bearish momentum.
Extreme Conditions: Z-Scores above 2.5 or below -2.0 highlight overbought or oversold markets, often preceding reversals.
Conclusion
The SOPR Z-Score indicator combines on-chain data with statistical analysis to provide traders with a clear, actionable view of market sentiment. Its customizable settings, visual clarity, and alert system make it an essential tool for both novice and experienced traders seeking an edge in the cryptocurrency markets.
Supply In Profit Z-Score | Vistula LabsOverview
The Supply In Profit Z-Score indicator is a Pine Script™ tool developed by Vistula Labs for technical analysis of cryptocurrencies, specifically Bitcoin (BTC) and Ethereum (ETH). It utilizes on-chain data from IntoTheBlock to calculate the difference between the percentage of addresses in profit and those in loss, transforming this metric into a Z-Score. This indicator helps traders identify market sentiment, trend-following opportunities, and overbought or oversold conditions.
What is Supply In Profit?
Supply In Profit is defined as the net difference between the percentage of addresses in profit and those in loss:
Profit Percentage: The proportion of addresses where the current value of holdings exceeds the acquisition price.
Loss Percentage: The proportion of addresses where the current value is below the acquisition price.
A positive value indicates more addresses are in profit, suggesting bullish sentiment, while a negative value indicates widespread losses, hinting at bearish sentiment.
How It Works
The indicator computes a Z-Score to normalize the Supply In Profit data relative to its historical behavior:
Z-Score = (Current Supply In Profit - Moving Average of Supply In Profit) / Standard Deviation of Supply In Profit
Current Supply In Profit: The latest profit-minus-loss percentage.
Moving Average: A customizable average (e.g., EMA, SMA) over a default 180-bar period.
Standard Deviation: Calculated over a default 200-bar lookback period.
Key Features
Data Source:
Selectable between BTC and ETH, pulling daily profit/loss percentage data from IntoTheBlock.
Customization:
Moving Average Type: Options include SMA, EMA, DEMA, RMA, WMA, or VWMA (default: EMA).
Moving Average Length: Default is 180 bars.
Z-Score Lookback: Default is 200 bars.
Thresholds: Adjustable for long/short signals and overbought/oversold levels.
Signals:
Long Signal: Z-Score crosses above the Long Threshold (default: 1.0).
Short Signal: Z-Score crosses below the Short Threshold (default: -0.64).
Overbought/Oversold Conditions:
Overbought: Z-Score > 3.0.
Oversold: Z-Score < -2.0.
Visualizations:
Z-Score Plot: Teal for long signals, magenta for short signals.
Threshold Lines: Dashed lines for long/short, solid lines for overbought/oversold.
Candlestick Coloring: Matches signal colors (teal/magenta).
Arrows: Green up-triangles for long entries, red down-triangles for short entries.
Background Colors: Magenta for overbought, teal for oversold.
Alerts:
Conditions for Long Opportunity, Short Opportunity, Overbought, and Oversold.
Usage Guide
Trend Following
Long Entry: When Z-Score crosses above 1.0, indicating potential upward momentum.
Short Entry: When Z-Score crosses below -0.64, suggesting potential downward momentum.
Overbought/Oversold Analysis
Overbought (Z-Score > 3.0): Consider profit-taking or preparing for a reversal.
Oversold (Z-Score < -2.0): Look for buying opportunities or exiting shorts.
Timeframe
Uses daily IntoTheBlock data, ideal for medium to long-term analysis.
Interpretation
High Z-Score: Indicates Supply In Profit is significantly above its historical mean, potentially signaling overvaluation.
Low Z-Score: Suggests Supply In Profit is below its mean, indicating possible undervaluation.
Signals and thresholds help traders act on shifts in market sentiment or extreme conditions.
Conclusion
The Supply In Profit Z-Score indicator provides a robust, data-driven approach to analyzing cryptocurrency market trends and sentiment. By combining on-chain metrics with statistical normalization, it empowers traders to make informed decisions based on historical context and current market dynamics.
BTC Dominance Excluding StablecoinsBTC Dominance Excluding Stablecoins
Description:
The "BTC Dominance Excluding Stablecoins" indicator calculates Bitcoin's dominance as a percentage of the total cryptocurrency market capitalization, excluding the market caps of major stablecoins (USDT and USDC). Unlike the standard BTC.D ticker, which includes stablecoins in the total market cap, this indicator provides a clearer view of Bitcoin’s dominance relative to the "non-stable" crypto market. This can be useful for traders and analysts who want to assess Bitcoin’s strength without the influence of stablecoin market caps, which often skew dominance metrics during periods of high stablecoin usage.
How It Works:
Bitcoin Market Cap: Fetches Bitcoin’s market capitalization using CRYPTOCAP:BTC.
Total Market Cap: Retrieves the total cryptocurrency market cap via CRYPTOCAP:TOTAL.
Stablecoin Adjustment: Subtracts the market caps of USDT (CRYPTOCAP:USDT) and USDC (CRYPTOCAP:USDC) from the total market cap.
Dominance Calculation: Computes Bitcoin’s dominance as (BTC Market Cap / Adjusted Total Market Cap) * 100, where the adjusted total excludes stablecoins.
Output: Plots the resulting dominance percentage as a line chart.
Features:
Displays Bitcoin dominance excluding stablecoins on any timeframe.
Customizable line color and thickness for better visualization.
Provides a more accurate representation of Bitcoin’s market share in the volatile, non-stablecoin crypto ecosystem.
Usage:
Add this indicator to your TradingView chart to compare Bitcoin’s dominance against the broader altcoin market, free from stablecoin distortions. Use it alongside other indicators like BTC.D or price charts to analyze market trends, especially during periods of high stablecoin inflows or outflows.
Notes:
The indicator currently excludes USDT and USDC, the two largest stablecoins by market cap. Additional stablecoins (e.g., DAI, BUSD) can be added by modifying the script if desired.
Data is sourced from TradingView’s CRYPTOCAP symbols, which may have slight delays or variations depending on exchange data feeds.
Best used on daily or higher timeframes for smoother, more reliable results.
Author:
Created by K Du₿
Version:
Pine Script v5
Breaking Structures (javieresfeliz)This TradingView script is designed to identify market structure changes, using a break of highs and lows approach, as well as technical indicators such as ATR, RSI, and EMAs (Exponential Moving Averages). It is aimed at detecting bullish and bearish trends, signaling possible entry and exit points based on various factors. It also offers additional confirmations to avoid false signals and provides a clear visualization of buy and sell signals.
Main Features:
Indicators Used:
ATR (Average True Range): Used to calculate a volatility range, which helps set stop-loss levels and price targets based on the current market volatility.
EMAs (50 and 200): Exponential Moving Averages (EMAs) are used to determine the short-term and long-term trends. The 50-period EMA is used to identify the short-term trend, while the 200-period EMA is used to identify the long-term trend.
RSI (Relative Strength Index): Used to identify overbought or oversold conditions in the market, providing additional buy or sell signals.
Volume: Used to confirm the validity of a signal. An increase in volume can confirm a structure break and provide more reliability to the signal.
Break of Structure Detection (BOS):
Bullish Break: Generated when the price surpasses previous highs.
Bearish Break: Generated when the price falls below previous lows.
Change of Character (CHOCH):
Bullish Trend: Defined by a close above the open and above the 50 EMA.
Bearish Trend: Defined by a close below the open and below the 50 EMA.
Buy and Sell Conditions:
Buy (Long): Activated when several conditions are met, including a bullish change of character, a bullish structure break, the price closing above the previous value plus a multiple of the ATR, and additional confirmations from RSI and volume.
Sell (Short): Activated when several conditions are met, including a bearish change of character, a bearish structure break, the price closing below the previous value minus a multiple of the ATR, with additional confirmations from RSI and volume.
Entry and Exit Signals:
Long Entry (Buy): Executed when the buy conditions are met.
Short Entry (Sell): Executed when the sell conditions are met.
Position Close: Positions are closed when the price crosses below (for long positions) or above (for short positions) the 50 EMA.
Historical Highs and Lows Lines:
The script draws lines of historical highs and lows from the last 288 and 60 periods to show key support and resistance levels on the chart.
Signal Table Across Multiple Timeframes:
The script displays a table in the top-right corner of the chart with indicators like the EMA trend, RSI value, and MACD histogram for timeframes of 1 minute, 5 minutes, 30 minutes, 1 hour, 4 hours, daily, and weekly.
Precautions:
Does not guarantee profits: Although the script is designed to detect structure breaks and possible trend changes, it does not guarantee 100% profitable signals. The market is always subject to risk and unpredictable volatility.
Requires adjustments for each asset: Parameters such as ATR length and EMA lengths should be adjusted according to the asset being analyzed and market conditions.
Use of additional confirmations: To reduce false signals, the script uses additional confirmations like RSI and volume, but it is always recommended to perform additional analysis before making trading decisions.
Changing trends: The change of character (CHOCH) can be a useful indicator, but it can give false signals in highly volatile markets or during prolonged consolidations.
Relies on historical data: This script relies on historical data to identify highs and lows. It does not consider fundamental events that may significantly impact the market.
Requires constant monitoring: Although the signals are automated, it is important to monitor open positions and make adjustments if market conditions change.
Risk of false signals: In low liquidity markets or consolidations, structure breaks can be false, so it’s recommended to pay attention to any additional confirmation signals or use a proper risk management strategy.
Volumen trend indicator 5MVOLUMEN TREND INDICATOR
Introduction
This indicator on TradingView provides a combination of technical analysis through a data table and visual elements on the chart. Its purpose is to provide a comprehensive view of the analyzed asset, facilitating decision-making.
How It Works
The indicator operates on two levels:
Data Table:
Displays key information about the asset's trend.
Includes metrics such as the current price, percentage change, volatility, and other relevant variables.
Can be customized to include additional indicators as needed.
Provides a quick analysis without the need to interpret complex charts.
Technical Elements on the Chart:
Incorporates dynamic support and resistance lines.
Can include moving averages, Bollinger Bands, RSI, or other custom indicators.
Offers visual alerts for significant changes in the asset's trend.
Facilitates detailed technical analysis through direct observation of patterns and signals.
Default Technical Indicators
The indicator comes with the following default pre-configured technical indicators:
Exponential Moving Average (EMA) 9:
This EMA responds more quickly to price movements, making it ideal for identifying short-term trends. It is generally used to detect crossovers with other EMAs or prices and is considered an entry or exit signal.
Exponential Moving Average (EMA) 21:
The 21-period EMA is used to identify medium-term trends. Its interaction with the 9 EMA is key to confirming buy or sell signals when both cross.
RSI (Relative Strength Index):
It is used to measure the magnitude of recent gains and losses of an asset, helping to identify overbought or oversold conditions.
Bollinger Bands:
These bands help identify volatility levels and potential reversal points. Price touching the upper or lower bands can be an important signal of trend change or continuation.
Customization
The user can modify several aspects of the indicator, such as:
Colors and styles of visual elements on the chart.
Types of indicators to include in the table.
Configuration of alerts and notifications.
Time interval for calculations and data updates.
EMA values (the periods can be changed if other configurations are desired).
Recommended Usage
To make the most of the indicator:
Use the data table to get an overview of the asset.
Analyze the technical elements on the chart to confirm trends.
Set alerts to avoid missing key opportunities.
Compare the information with other indicators and data sources before making decisions.
Precautions and Best Practices
Avoid relying solely on the indicator: Complement it with other technical and fundamental analysis.
Adjust the settings according to the asset's volatility: Not all strategies work the same across different markets.
Don’t overload the chart with too many elements: This can create visual noise and confusion in interpretation.
Test it on a demo account before trading live: To familiarize yourself with the indicator's functionality and adjustments.
----------------------------------------------
Remember that no system is perfect, keep these considerations in mind for this indicator:
Do not trade when a signal appears during an opposite trend:
Do not trade when the market is uncertain in its direction or within a parallel channel:
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
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.
IBD Style Relative Strength RatingWelcome to the IBD Style Relative Strength Rating Indicator!
A powerful tool inspired by Investor's Business Daily (IBD), this indicator helps traders evaluate stock performance relative to a benchmark. It’s perfect for identifying strong or weak stocks compared to the broader market, specifically the S&P 500 (SPY). Whether you're a beginner or an experienced investor, this guide will walk you through its features and key concepts, including the RS Line and RS Rating, and how legendary trader Mark Minervini uses similar tools.
Understanding the RS Line & RS Rating
RS Line (Relative Strength Line)
A visual representation of how a stock’s price performs relative to SPY.
Calculated by dividing the stock’s closing price by SPY’s closing price and multiplying by 100.
Rising RS Line → Stock is outperforming SPY.
Falling RS Line → Stock is underperforming SPY.
Helps identify strength or weakness compared to the market.
RS Rating
A numerical score (1-99) measuring stock performance over 252 trading days (1 year) relative to SPY.
Above 80 → Top 20% of performers.
Above 90 → Top 10% (ideal for growth investors).
Weighted average of stock’s price changes over 63, 126, 189, and 252 days.
Key Features Explained
RS Line Color Mode:
Static (default white) or Dynamic (green when rising, red when falling) for quick trend identification.
Comparative Symbol:
Default: SPY. Can be changed to NASDAQ:NDX, AAPL, or other indices/stocks.
Ensure selected symbols have sufficient historical data.
Plot RS New Highs: Marks new 250-day highs with subtle blue circles
Indicates a stock significantly outperforming SPY (potential buy signal).
Plot RS New Lows: Marks new 250-day lows with red circles
Signals underperformance (possible sell or avoid indicator).
Lookback for Display: Adjustable up to 2000 bars for historical trend analysis.
RS Rating Color Scheme
Green: Upward trend (improving RS Rating).
Orange: Neutral/mixed trend.
Red: Downward trend (declining RS Rating).
Dynamic Color Settings
Rising Line Color: Green (default), customizable.
Falling Line Color: Red (default), adjustable.
Advanced Options
Enable Replay Mode: Uses fixed percentile values for consistent RS Rating calculations in backtesting.
RS Rating Table
Displays current RS Rating and values from previous day, week, and month in the top-right corner (daily charts).
Background color reflects trend: Green (up), Orange (neutral), Red (down).
Past values appear in neutral gray for a quick performance snapshot.
How Mark Minervini Uses This Indicator
Mark Minervini, a legendary trader, emphasizes Relative Strength as a core strategy:
Looks for stocks with:
Rising RS Line.
RS Rating above 80-90 (top performers).
RS New Highs to spot breakout candidates.
Avoids stocks with:
Declining RS Line.
RS Rating below 70.
Important Information for Beginners
RS vs. SPY
The indicator compares stock performance against SPY (S&P 500).
Rising RS Line → Stock is beating SPY.
Falling RS Line → Stock is lagging.
Why Use This Indicator?
Helps find strong relative strength stocks, crucial for bullish trends.
New highs/lows on the RS Line signal significant shifts.
The RS Rating quantifies percentile-based performance.
Customization Options
Adjust colors, lookback periods, and marker sizes to match your trading style.
Default SPY comparison is ideal for U.S. traders but can be customized.
Timeframe Considerations
Optimized for daily charts.
Weekly/monthly charts may have limited data availability.
Tips for Crypto Traders (Measuring Altcoins vs. Bitcoin or Total Market Cap)
If trading cryptocurrencies, this indicator can measure altcoins vs. Bitcoin (BTC) or the total crypto market cap (TOTAL):
Comparative Symbol Setup:
Set Comparative Symbol to BTCUSD to compare an altcoin (e.g., ETHUSD) against Bitcoin.
Rising RS Line → The altcoin is outperforming Bitcoin (bullish signal).
Use TOTAL (crypto market cap index) to assess an altcoin’s strength against the total market.
High RS Rating suggests the altcoin is a market leader.
Adjust Look-back Periods:
Crypto markets are volatile, so reduce Look-back for New Highs/Lows to 50-100 bars (about 2-4 months) for shorter-term trends.
Fine-tune based on your trading strategy.
New Highs and Lows:
Watch for new RS Line highs (blue dots) to identify altcoins breaking out against BTC or TOTAL (momentum trading).
New lows (red dots) may signal weakening altcoins to avoid.
RS Rating Interpretation:
Above 80 against BTC or TOTAL → The altcoin is a strong performer.
This aligns with Minervini’s growth strategy for stocks.
Color Dynamics:
Use Dynamic RS Line Color (green for rising, red for falling) to quickly spot altcoin trends against BTC or TOTAL.
Crypto data may have gaps—test indicator settings on different timeframes (e.g., 1-hour or 4-hour charts).
Tips for Getting Started
Apply the Indicator to a stock chart and set Comparative Symbol to SPY.
Watch the RS Line:
If trending upward with new highs and RS Rating > 80, it's a strong candidate.
Use the RS Rating Table to check for trend consistency.
Adjust Opacity Settings for markers to balance visibility and clarity.
This indicator is now ready for public use as of March 18, 2025. Enjoy trading with enhanced insights, and feel free to share feedback or suggestions for future updates!
Btc and Eth 5 min winnerWhat the Strategy Does
Finding the Trend (Like Watching the Bus Move): The strategy uses special tools called Hull Moving Averages (HMAs) to figure out if Bitcoin (BTC) Ethereum (ETH) prices are generally going up or down. It looks at short-term (5 minutes) and long-term (10 minutes) price movements to make sure the “bus” (the market) is moving strongly in one direction—up for buying, down for selling.
Spotting Good Times to Jump On (Buy or Sell Signals): It looks for two types of opportunities:
Pullbacks: When the price dips a little while still moving up (like the bus slowing down but not stopping), it’s a chance to buy.
Breakouts: When the price suddenly jumps higher after being stuck (like the bus speeding up), it’s another chance to buy. It does the opposite for selling when prices are dropping.
It also checks if there’s enough “passenger activity” (volume) and momentum (speed of price change) to make sure it’s a good move.
Avoiding Traffic Jams (Filters): The strategy uses tools like RSI (to check if the market’s too fast or too slow), volume (to see if enough people are trading), and ATR (to measure how wild the price swings are). It skips trades if things look too chaotic or if the trend isn’t strong enough.
Setting Safety Stops and Profit Targets: Once you’re on the “bus,” it sets rules to protect you:
Stop-Loss: If the price moves against you by a small amount (0.5% of the typical price swing), you jump off to avoid losing too much—think of it as getting off before the bus crashes.
Take-Profit: If the price moves in your favor by a small amount (1.0% of the typical swing), you cash out—imagine getting off at your stop with a profit.
Trailing Stop: If the price keeps moving your way, it adjusts your exit point to lock in more profit, like moving your stop closer as the bus keeps going.
Using Leverage (10x Boost): This strategy uses 10x leverage on Binance futures, meaning for every $1 you have, you trade like you have $10. This can make profits (or losses) 10 times bigger, so it’s risky but can be rewarding if you’re careful.
Why 5 Minutes and Bitcoin and Ethereum?
5-Minute Chart: This is like checking the bus every 5 minutes to make quick, small trades—perfect for fast, short profits.
Bitcoin Ethereum (BTC/USD)(ETH/USD): It’s the most popular and liquid crypto, so there’s lots of activity, making it easier to jump on and off without getting stuck.
Why It Aims for 90% Wins (But Be Realistic)
The goal is to win 9 out of 10 trades by being super picky about when to trade—only jumping on when the trend, momentum, and volume are all perfect. But in real trading, markets can be unpredictable, so 90% is very hard to achieve. Still, this strategy tries to be as accurate as possible by avoiding bad moves and focusing on strong trends.
Risks for a New Trader
Leverage: Trading with 10x leverage means small price moves can lead to big losses if you’re not careful. Start with a demo account (pretend money) on TradingView or Binance to practice.
Learning Curve: This strategy uses technical terms (like HMAs, RSI) and tools you’ll need to learn over time. Don’t rush—just practice and ask questions!
How to Use It
Go to TradingView, load this strategy on a 5-minute BTC/USD futures chart on Binance.
Watch the green triangles (buy signals) and red triangles (sell signals) on the chart—they tell you when to trade.
Use the stops and targets to manage your trades—don’t guess, let the strategy guide you.
Start small, learn from each trade, and don’t risk money you can’t afford to lose.
This is like learning to ride a bike—start slow, practice, and you’ll get better. If you have more questions or want simpler tips, feel free to ask! Trading can be fun and rewarding, but it takes patience and practice.
TSI Long/Short for BTC 2HThe TSI Long/Short for BTC 2H strategy is an advanced trend-following system designed specifically for trading Bitcoin (BTC) on a 2-hour timeframe. It leverages the True Strength Index (TSI) to identify momentum shifts and executes both long and short trades in response to dynamic market conditions.
Unlike traditional moving average-based strategies, this script uses a double-smoothed momentum calculation, enhancing signal accuracy and reducing noise. It incorporates automated position sizing, customizable leverage, and real-time performance tracking, ensuring a structured and adaptable trading approach.
🔹 What Makes This Strategy Unique?
Unlike simple crossover strategies or generic trend-following approaches, this system utilizes a customized True Strength Index (TSI) methodology that dynamically adjusts to market conditions.
🔸 True Strength Index (TSI) Filtering – The script refines the TSI by applying double exponential smoothing, filtering out weak signals and capturing high-confidence momentum shifts.
🔸 Adaptive Entry & Exit Logic – Instead of fixed thresholds, it compares the TSI value against a dynamically determined high/low range from the past 100 bars to confirm trade signals.
🔸 Leverage & Risk Optimization – Position sizing is dynamically adjusted based on account equity and leverage settings, ensuring controlled risk exposure.
🔸 Performance Monitoring System – A built-in performance tracking table allows traders to evaluate monthly and yearly results directly on the chart.
📊 Core Strategy Components
1️⃣ Momentum-Based Trade Execution
The strategy generates long and short trade signals based on the following conditions:
✅ Long Entry Condition – A buy signal is triggered when the TSI crosses above its 100-bar highest value (previously set), confirming bullish momentum.
✅ Short Entry Condition – A sell signal is generated when the TSI crosses below its 100-bar lowest value (previously set), indicating bearish pressure.
Each trade execution is fully automated, reducing emotional decision-making and improving trading discipline.
2️⃣ Position Sizing & Leverage Control
Risk management is a key focus of this strategy:
🔹 Dynamic Position Sizing – The script calculates position size based on:
Account Equity – Ensuring trade sizes adjust dynamically with capital fluctuations.
Leverage Multiplier – Allows traders to customize risk exposure via an adjustable leverage setting.
🔹 No Fixed Stop-Loss – The strategy relies on reversals to exit trades, meaning each position is closed when the opposite signal appears.
This design ensures maximum capital efficiency while adapting to market conditions in real time.
3️⃣ Performance Visualization & Tracking
Understanding historical performance is crucial for refining strategies. The script includes:
📌 Real-Time Trade Markers – Buy and sell signals are visually displayed on the chart for easy reference.
📌 Performance Metrics Table – Tracks monthly and yearly returns in percentage form, helping traders assess profitability over time.
📌 Trade History Visualization – Completed trades are displayed with color-coded boxes (green for long trades, red for short trades), visually representing profit/loss dynamics.
📢 Why Use This Strategy?
✔ Advanced Momentum Detection – Uses a double-smoothed TSI for more accurate trend signals.
✔ Fully Automated Trading – Removes emotional bias and enforces discipline.
✔ Customizable Risk Management – Adjust leverage and position sizing to suit your risk profile.
✔ Comprehensive Performance Tracking – Integrated reporting system provides clear insights into past trades.
This strategy is ideal for Bitcoin traders looking for a structured, high-probability system that adapts to both bullish and bearish trends on the 2-hour timeframe.
📌 How to Use: Simply add the script to your 2H BTC chart, configure your leverage settings, and let the system handle trade execution and tracking! 🚀
Excess Liquidity IndicatorExcess Liquidity Indicator
This script visualizes excess liquidity trends in relation to risk assets. It estimates excess liquidity by combining various macroeconomic factors such as WW M2 money supply, central bank balance sheets, and interest rates, oil, and the dollar index, and it substracts WW GDP. The tool helps traders analyze liquidity-driven market trends in a structured manner.
Note: This script is for research purposes only and does not provide financial advice.
I cannot point names cause I get banned but work is inspired by others...
Ultimate Volatility Scanner by NHBprod - Requested by Client!Hey Everyone!
I created another script to add to my growing library of strategies and indicators that I use for automated crypto and stock trading! This strategy is for BITCOIN but can be used on any stock or crypto. This was requested by a client so I thought I should create it and hopefully build off of it and build variants!
This script gets and compares the 14-day volatility using the ATR percentage for a list of cryptocurrencies and stocks. Cryptocurrencies are preloaded into the script, and the script will show you the TOP 5 coins in terms of volatility, and then compares it to the Bitcoin volatility as a reference. It updates these values once per day using daily timeframe data from TradingView. The coins are then sorted in descending order by their volatility.
If you don't want to use the preloaded set of coins, you have the option of inputting your own coins AND/OR stocks!
Let me know your thoughts.
