Quadratic Regression Slope [DW]This is a study geared toward identifying price trends using Quadratic regression.
Quadratic regression is the process of finding the equation of a parabola that best fits the set of data being analyzed.
In this study, first a quadratic regression curve is calculated, then the slope of the curve is calculated and plotted.
Custom bar colors are included. The color scheme is based on whether the slope is positive or negative, and whether it is increasing or decreasing.
Recherche dans les scripts pour "curve"
XPloRR MA-Trailing-Stop StrategyXPloRR MA-Trailing-Stop Strategy
Long term MA-Trailing-Stop strategy with Adjustable Signal Strength to beat Buy&Hold strategy
None of the strategies that I tested can beat the long term Buy&Hold strategy. That's the reason why I wrote this strategy.
Purpose: beat Buy&Hold strategy with around 10 trades. 100% capitalize sold trade into new trade.
My buy strategy is triggered by the fast buy EMA (blue) crossing over the slow buy SMA curve (orange) and the fast buy EMA has a certain up strength.
My sell strategy is triggered by either one of these conditions:
the EMA(6) of the close value is crossing under the trailing stop value (green) or
the fast sell EMA (navy) is crossing under the slow sell SMA curve (red) and the fast sell EMA has a certain down strength.
The trailing stop value (green) is set to a multiple of the ATR(15) value.
ATR(15) is the SMA(15) value of the difference between the high and low values.
The scripts shows a lot of graphical information:
The close value is shown in light-green. When the close value is lower then the buy value, the close value is shown in light-red. This way it is possible to evaluate the virtual losses during the trade.
the trailing stop value is shown in dark-green. When the sell value is lower then the buy value, the last color of the trade will be red (best viewed when zoomed)(in the example, there are 2 trades that end in gain and 2 in loss (red line at end))
the EMA and SMA values for both buy and sell signals are shown as a line
the buy and sell(close) signals are labeled in blue
How to use this strategy?
Every stock has it's own "DNA", so first thing to do is tune the right parameters to get the best strategy values voor EMA , SMA, Strength for both buy and sell and the Trailing Stop (#ATR).
Look in the strategy tester overview to optimize the values Percent Profitable and Net Profit (using the strategy settings icon, you can increase/decrease the parameters)
Then keep using these parameters for future buy/sell signals only for that particular stock.
Do the same for other stocks.
Important : optimizing these parameters is no guarantee for future winning trades!
Here are the parameters:
Fast EMA Buy: buy trigger when Fast EMA Buy crosses over the Slow SMA Buy value (use values between 10-20)
Slow SMA Buy: buy trigger when Fast EMA Buy crosses over the Slow SMA Buy value (use values between 30-100)
Minimum Buy Strength: minimum upward trend value of the Fast SMA Buy value (directional coefficient)(use values between 0-120)
Fast EMA Sell: sell trigger when Fast EMA Sell crosses under the Slow SMA Sell value (use values between 10-20)
Slow SMA Sell: sell trigger when Fast EMA Sell crosses under the Slow SMA Sell value (use values between 30-100)
Minimum Sell Strength: minimum downward trend value of the Fast SMA Sell value (directional coefficient)(use values between 0-120)
Trailing Stop (#ATR): the trailing stop value as a multiple of the ATR(15) value (use values between 2-20)
Example parameters for different stocks (Start capital: 1000, Order=100% of equity, Period 1/1/2005 to now) compared to the Buy&Hold Strategy(=do nothing):
BEKB(Bekaert): EMA-Buy=12, SMA-Buy=44, Strength-Buy=65, EMA-Sell=12, SMA-Sell=55, Strength-Sell=120, Stop#ATR=20
NetProfit: 996%, #Trades: 6, %Profitable: 83%, Buy&HoldProfit: 78%
BAR(Barco): EMA-Buy=16, SMA-Buy=80, Strength-Buy=44, EMA-Sell=12, SMA-Sell=45, Strength-Sell=82, Stop#ATR=9
NetProfit: 385%, #Trades: 7, %Profitable: 71%, Buy&HoldProfit: 55%
AAPL(Apple): EMA-Buy=12, SMA-Buy=45, Strength-Buy=40, EMA-Sell=19, SMA-Sell=45, Strength-Sell=106, Stop#ATR=8
NetProfit: 6900%, #Trades: 7, %Profitable: 71%, Buy&HoldProfit: 2938%
TNET(Telenet): EMA-Buy=12, SMA-Buy=45, Strength-Buy=27, EMA-Sell=19, SMA-Sell=45, Strength-Sell=70, Stop#ATR=14
NetProfit: 129%, #Trade
Donchian Channel Trend Intensity [DW]This is an experimental study designed to analyze trend intensity using two Donchian Channels.
The DCTI curve is calculated by comparing the differences between Donchian highs and lows over a major an minor period, and expressing them as a positive and negative percentage.
The curve is then smoothed with an exponential moving average to provide a signal line.
Custom bar colors included with two coloring methods to choose from.
AWESOME OSCILLATOR V2 by KIVANCfr3762AWESOME OSCILLATOR V2 by KIVANC @fr3762
CONVERTING THE OSCILLATOR to a curved line and added a 7 period SMA as a signal line,
crosses are BUY or SELL signals like in MACD
Buy: when AO line crosses above signal line
Sell: when Signal line crosses above AO line
Stefan Krecher: Jeddingen DivergenceThe main idea is to identify a divergence between momentum and price movement. E.g. if the momentum is rising but price is going down - this is what we call a divergence. The divergence will be calculated by comparing the direction of the linear regression curve of the price with the linear regression curve of momentum.
A bearish divergence can be identified by a thick red line, a bullish divergence by a green line.
When there is a divergence, it is likeley that the current trend will change it's direction.
Looking at the chart, there are three divergences that need to get interpreted:
1) bearish divergence, RSI is overbought but MACD does not clearly indicate a trend change. Right after the divergence, price and momentum are going up. No clear signal for a sell trade
2) bearish divergence, RSI still overbought, MACD histogram peaked, MACD crossed the signal line, price and momentum are going down. Very clear constellation for a sell trade.
3) two bullish diverences, RSI is oversold, MACD crossover near the end of the second divergence, price and momentum started rising. Good constellation for a buy trade. Could act as exit signal for the beforementioned sell trade.
More information on the Jeddingen Divergence is available here: www.forexpython.com
Power Law Correlation Indicator 2.0 The Power Law Correlation Indicator is an attempt to chart when a stock/currency/futures contract goes parabolic forming a upward or downward curve that accelerates according to power laws.
I've read about power laws from Sornette Diedler ( www.marketcalls.in ). And I think the theory is a good one.
The idea behind this indicator is that it will rise to 1.0 as the curve resembles a parabolic up or down swing. When it is below zero, the stock will flatten out.
There are many ways to use this indicator. One way I am testing it out is in trading Strangles or Straddle option trades. When this indicator goes below zero and starts to turn around, it means that it has flattened out. This is like a squeeze indicator. (see the TTM squeeze indicator).
Since this indicator goes below zero and the squeeze plays tend to be mean-reverting; then its a great time to put on a straddle/strangle.
Another way to use it is to think of it in terms of trend strength. Think of it as a kind of ADX, that measures the trend strength. When it is rising, the trend is strong; when it is dropping, the trend is weak.
Lastly I think this indicator needs some work. I tried to put the power (x^n) function into it but my coding skill is limited. I am hoping that Lazy Bear or Ricardo Santos can do it some justice.
Also I think that if we can figure out how to do other power law graphs, perhaps we can plot them together on one indicator.
So far I really like this one for finding Strangle spots. So check it out.
Peace
SpreadEagle71
Impulse Reactor RSI-SMA Trend Indicator [ApexLegion]Impulse Reactor RSI-SMA Trend Indicator
Introduction and Theoretical Background
Design Rationale
Standard indicators frequently generate binary 'BUY' or 'SELL' signals without accounting for the broader market context. This often results in erratic "Flip-Flop" behavior, where signals are triggered indiscriminately regardless of the prevailing volatility regime.
Impulse Reactor was engineered to address this limitation by unifying two critical requirements: Quantitative Rigor and Execution Flexibility.
The Solution
Composite Analytical Framework This script is not a simple visual overlay of existing indicators. It is an algorithmic synthesis designed to function as a unified decision-making engine. The primary objective was to implement rigorous quantitative analysis (Volatility Normalization, Structural Filtering) directly within an alert-enabled framework. This architecture is designed to process signals through strict, multi-factor validation protocols before generating real-time notifications, allowing users to focus on structurally validated setups without manual monitoring.
How It Works
This is not a simple visual mashup. It utilizes a cross-validation algorithm where the Trend Structure acts as a gatekeeper for Momentum signals:
Logic over Lag: Unlike simple moving average crossovers, this script uses a 15-layer Gradient Ribbon to detect "Laminar Flow." If the ribbon is knotted (Compression), the system mathematically suppresses all signals.
Volatility Normalization: The core calculation adapts to ATR (Average True Range). This means the indicator automatically expands in volatile markets and contracts in quiet ones, maintaining accuracy without constant manual tweaking.
Adaptive Signal Thresholding: It incorporates an 'Anti-Greed' algorithm (Dynamic Thresholding) that automatically adjusts entry criteria based on trend duration. This logic aims to mitigate the risk of entering positions during periods of statistical trend exhaustion.
Why Use It?
Market State Decoding: The gradient Ribbon visualizes the underlying trend phase in real-time.
◦ Cyan/Blue Flow: Strong Bullish Trend (Laminar Flow).
◦ Magenta/Pink Flow: Strong Bearish Trend.
◦ Compressed/Knotted: When the ribbon lines are tightly squeezed or overlapping, it signals Consolidation. The system filters signals here to avoid chop.
Noise Reduction: The goal is not to catch every pivot, but to isolate high-confidence setups. The logic explicitly filters out minor fluctuations to help maintain position alignment with the broader trend.
⚖️ Chapter 1: System Architecture
Introduction: Composite Analytical Framework
System Overview
Impulse Reactor serves as a comprehensive technical analysis engine designed to synthesize three distinct market dimensions—Momentum, Volatility, and Trend Structure—into a unified decision-making framework. Unlike traditional methods that analyze these metrics in isolation, this system functions as a central processing unit that integrates disparate data streams to construct a coherent model of market behavior.
Operational Objective
The primary objective is to transition from single-dimensional signal generation to a multi-factor assessment model. By fusing data from the Impulse Core (Volatility), Gradient Oscillator (Momentum), and Structural Baseline (Trend), the system aims to filter out stochastic noise and identify high-probability trade setups grounded in quantitative confluence.
Market Microstructure Analysis: Limitations of Conventional Models
Extensive backtesting and quantitative analysis have identified three critical inefficiencies in standard oscillator-based strategies:
• Bounded Oscillator Limitations (The "Oscillation Trap"): Traditional indicators such as RSI or Stochastics are mathematically constrained between fixed values (0 to 100). In strong trending environments, these metrics often saturate in "overbought" or "oversold" zones. Consequently, traders relying on static thresholds frequently exit structurally valid positions prematurely or initiate counter-trend trades against prevailing momentum, resulting in suboptimal performance.
• Quantitative Blindness to Quality: Standard moving averages and trend indicators often fail to distinguish the qualitative nature of price movement. They treat low-volume drift and high-velocity expansion identically. This inability to account for "Volatility Quality" leads to delayed responsiveness during critical market events.
• Fractal Dissonance (Timeframe Disconnect): Financial markets exhibit fractal characteristics where trends on lower timeframes may contradict higher timeframe structures. Manual integration of multi-timeframe analysis increases cognitive load and susceptibility to human error, often resulting in conflicting biases at the point of execution.
Core Design Principles
To mitigate the aforementioned systemic inefficiencies, Impulse Reactor employs a modular architecture governed by three foundational principles:
Principle A:
Volatility Precursor Analysis Market mechanics demonstrate that volatility expansion often functions as a leading indicator for directional price movement. The system is engineered to detect "Volatility Deviation" — specifically, the divergence between short-term and long-term volatility baselines—prior to its manifestation in price action. This allows for entry timing aligned with the expansion phase of market volatility.
Principle B:
Momentum Density Visualization The system replaces singular momentum lines with a "Momentum Density" model utilizing a 15-layer Simple Moving Average (SMA) Ribbon.
• Concept: This visualization represents the aggregate strength and consistency of the trend.
• Application: A fully aligned and expanded ribbon indicates a robust trend structure ("Laminar Flow") capable of withstanding minor counter-trend noise, whereas a compressed ribbon signals consolidation or structural weakness.
Principle C:
Adaptive Confluence Protocols Signal validity is strictly governed by a multi-dimensional confluence logic. The system suppresses signal generation unless there is synchronized confirmation across all three analytical vectors:
1. Volatility: Confirmed expansion via the Impulse Core.
2. Momentum: Directional alignment via the Hybrid Oscillator.
3. Structure: Trend validation via the Baseline. This strict filtering mechanism significantly reduces false positives in non-trending (choppy) environments while maintaining sensitivity to genuine breakouts.
🔍 Chapter 2: Core Modules & Algorithmic Logic
Module A: Impulse Core (Normalized Volatility Deviation)
Operational Logic The Impulse Core functions as a volatility-normalized momentum gauge rather than a standard oscillator. It is designed to identify "Volatility Contraction" (Squeeze) and "Volatility Expansion" phases by quantifying the divergence between short-term and long-term volatility states.
Volatility Z-Score Normalization
The formula implements a custom normalization algorithm. Unlike standard oscillators that rely on absolute price changes, this logic calculates the Z-Score of the Volatility Spread.
◦ Numerator: (atr_f - atr_s) captures the raw momentum of volatility expansion.
◦ Denominator: (std_f + 1e-6) standardizes this value against historical variance.
◦ Result: This allows the indicator scales consistently across assets (e.g., Bitcoin vs. Euro) without manual recalibration.
f_impulse() =>
atr_f = ta.atr(fastLen) // Fast Volatility Baseline
atr_s = ta.atr(slowLen) // Slow Volatility Baseline
std_f = ta.stdev(atr_f, devLen) // Volatility Standard Deviation
(atr_f - atr_s) / (std_f + 1e-6) // Normalized Differential Calculation
Algorithmic Framework
• Differential Calculation: The system computes the spread between a Fast Volatility Baseline (ATR-10) and a Slow Volatility Baseline (ATR-30).
• Normalization Protocol: To standardize consistency across diverse asset classes (e.g., Forex vs. Crypto), the raw differential is divided by the standard deviation of the volatility itself over a 30-period lookback.
• Signal Generation:
◦ Contraction (Squeeze): When the Fast ATR compresses below the Slow ATR, it registers a potential volatility buildup phase.
◦ Expansion (Release): A rapid divergence of the Fast ATR above the Slow ATR signals a confirmed volatility expansion, validating the strength of the move.
Module B: Gradient Oscillator (RSI-SMA Hybrid)
Design Rationale To mitigate the "noise" and "false reversal" signals common in single-line oscillators (like standard RSI), this module utilizes a 15-Layer Gradient Ribbon to visualize momentum density and persistence.
Technical Architecture
• Ribbon Array: The system generates 15 sequential Simple Moving Averages (SMA) applied to a volatility-adjusted RSI source. The length of each layer increases incrementally.
• State Analysis:
Momentum Alignment (Laminar Flow): When all 15 layers are expanded and parallel, it indicates a robust trend where buying/selling pressure is distributed evenly across multiple timeframes. This state helps filter out premature "overbought/oversold" signals.
• Consolidation (Compression): When the distance between the fastest layer (Layer 1) and the slowest layer (Layer 15) approaches zero or the layers intersect, the system identifies a "Non-Tradable Zone," preventing entries during choppy market conditions.
// Laminar Flow Validation
f_validate_trend() =>
// Calculate spread between Ribbon layers
ribbon_spread = ta.stdev(ribbon_array, 15)
// Only allow signals if Ribbon is expanded (Laminar Flow)
is_flowing = ribbon_spread > min_expansion_threshold
// If compressed (Knotted), force signal to false
is_flowing ? signal : na
Module C: Adaptive Signal Filtering (Behavioral Bias Mitigation)
This subsystem, operating as an algorithmic "Anti-Greed" Mechanism, addresses the statistical tendency for signal degradation following prolonged trends.
Dynamic Threshold Adjustment
• Win Streak Detection: The algorithm internally tracks the outcome of closed trade cycles.
• Sensitivity Multiplier: Upon detecting consecutive successful signals in the same direction, a Penalty_Factor is applied to the entry logic.
• Operational Impact: This effectively raises the Required_Slope threshold for subsequent signals. For example, after three consecutive bullish signals, the system requires a 30% steeper trend angle to validate a fourth entry. This enforces stricter discipline during extended trends to reduce the probability of entering at the point of trend exhaustion.
Anti-Greed Logic: Dynamic Threshold Calculation
f_adjust_threshold(base_slope, win_streak) =>
// Adds a 10% penalty to the difficulty for every consecutive win
penalty_factor = 0.10
risk_scaler = 1 + (win_streak * penalty_factor)
// Returns the new, harder-to-reach threshold
base_slope * risk_scaler
Module D: Trend Baseline (Triple-Smoothed Structure)
The Trend Baseline serves as the structural filter for all signals. It employs a Triple-Smoothed Hybrid Algorithm designed to balance lag reduction with noise filtration.
Smoothing Stages
1. Volatility Banding: Utilizes a SuperTrend-based calculation to establish the upper and lower boundaries of price action.
2. Weighted Filter: Applies a Weighted Moving Average (WMA) to prioritize recent price data.
3. Exponential Smoothing: A final Exponential Moving Average (EMA) pass is applied to create a seamless baseline curve.
Functionality
This "Heavy" baseline resists minor intraday volatility spikes while remaining responsive to sustained structural shifts. A signal is only considered valid if the price action maintains structural integrity relative to this baseline
🚦 Chapter 3: Risk Management & Exit Protocols
Quantitative Risk Management (TP/SL & Trailing)
Foundational Architecture: Volatility-Adjusted Geometry Unlike strategies relying on static nominal values, Impulse Reactor establishes dynamic risk boundaries derived from quantitative volatility metrics. This design aligns trade invalidation levels mathematically with the current market regime.
• ATR-Based Dynamic Bracketing:
The protocol calculates Stop-Loss and Take-Profit levels by applying Fibonacci coefficients (Default: 0.786 for SL / 1.618 for TP) to the Average True Range (ATR).
◦ High Volatility Environments: The risk bands automatically expand to accommodate wider variance, preventing premature exits caused by standard market noise.
◦ Low Volatility Environments: The bands contract to tighten risk parameters, thereby dynamically adjusting the Risk-to-Reward (R:R) geometry.
• Close-Validation Protocol ("Soft Stop"):
Institutional algorithms frequently execute liquidity sweeps—driving prices briefly below key support levels to accumulate inventory.
◦ Mechanism: When the "Soft Stop" feature is enabled, the system filters out intraday volatility spikes. The stop-loss is conditional; execution is triggered only if the candle closes beyond the invalidation threshold.
◦ Strategic Advantage: This logic distinguishes between momentary price wicks and genuine structural breakdowns, preserving positions during transient volatility.
• Step-Function Trailing Mechanism:
To protect unrealized PnL while allowing for normal price breathing, a two-phase trailing methodology is employed:
◦ Phase 1 (Activation): The trailing function remains dormant until the price advances by a pre-defined percentage threshold.
◦ Phase 2 (Dynamic Floor): Once armed, the stop level creates a moving floor, adjusting relative to price action while maintaining a volatility-based (ATR) buffer to systematically protect unrealized PnL.
• Algorithmic Exit Protocols (Dynamic Liquidity Analysis)
◦ Rationale: Inefficiencies of Static Targets Static "Take Profit" levels often result in suboptimal exits. They compel traders to close positions based on arbitrary figures rather than evolving market structure, potentially capping upside during significant trends or retaining positions while the underlying trend structure deteriorates.
◦ Solution: Structural Integrity Assessment The system utilizes a Dynamic Liquidity Engine to continuously audit the validity of the position. Instead of targeting a specific price point, the algorithm evaluates whether the trend remains statistically robust.
Multi-Factor Exit Logic (The Tri-Vector System)
The Smart Exit protocol executes only when specific algorithmic invalidation criteria are met:
• 1. Momentum Exhaustion (Confluence Decay): The system monitors a 168-hour rolling average of the Confluence Score. A significant deviation below this historical baseline indicates momentum exhaustion, signaling that the driving force behind the trend has dissipated prior to a price reversal. This enables preemptive exits before a potential drawdown.
• 2. Statistical Over-Extension (Mean Reversion): Utilizing the core volatility logic, the system identifies instances where price deviates beyond 2.0 standard deviations from the mean. While the trend may be technically bullish, this statistical anomaly suggests a high probability of mean reversion (elastic snap-back), triggering a defensive exit to capitalize on peak valuation.
• 3. Oscillator Rejection (Immediate Pivot): To manage sudden V-shaped volatility, the system monitors RSI pivots. If a sharp "Pivot High" or divergence is detected, the protocol triggers an immediate "Peak Exit," bypassing standard trend filters to secure liquidity during high-velocity reversals.
🎨 Chapter 4: Visualization Guide
Gradient Oscillator Ribbon
The 15-layer SMA ribbon visualized via plot(r1...r15) represents the "Momentum Density" of the market.
• Visuals:
◦ Cyan/Blue Ribbon: Indicates Bullish Momentum.
◦ Pink/Magenta Ribbon: Indicates Bearish Momentum.
• Interpretation:
◦ Laminar Flow: When the ribbon expands widely and flows in parallel, it signifies a robust trend where momentum is distributed evenly across timeframes. This is the ideal state for trend-following.
◦ Compression (Consolidation): If the ribbon becomes narrow, twisted, or knotted, it indicates a "Non-Tradable Zone" where the market lacks a unified direction. Traders are advised to wait for clarity.
◦ Over-Extension: If the top layer crosses the Overbought (85) or Oversold (15) lines, it visually warns of potential market overheating.
Trend Baseline
The thick, color-changing line plotted via plot(baseline) represents the Structural Backbone of the market.
• Visuals: Changes color based on the trend direction (Blue for Bullish, Pink for Bearish).
• Interpretation:
Structural Filter: Long positions are statistically favored only when price action sustains above this baseline, while short positions are favored below it.
Dynamic Support/Resistance: The baseline acts as a dynamic support level during uptrends and resistance during downtrends.
Entry Signals & Labels
Text labels ("Long Entry", "Short Entry") appear when the system detects high-probability setups grounded in quantitative confluence.
• Visuals: Labeled signals appear above/below specific candles.
• Interpretation:
These signals represent moments where Volatility (Expansion), Momentum (Alignment), and Structure (Trend) are synchronized.
Smart Exit: Labels such as "Smart Exit" or "Peak Exit" appear when the system detects momentum exhaustion or structural decay, prompting a defensive exit to preserve capital.
Dynamic TP/SL Boxes
The semi-transparent colored zones drawn via fill() represent the risk management geometry.
• Visuals: Colored boxes extending from the entry point to the Take Profit (TP) and Stop Loss (SL) levels.
• Function:
Volatility-Adjusted Geometry: Unlike static price targets, these boxes expand during high volatility (to prevent wicks from stopping you out) and contract during low volatility (to optimize Risk-to-Reward ratios).
SAR + MACD Glow
Small glowing shapes appearing above or below candles.
• Visuals: Triangle or circle glows near the price bars.
• Interpretation:
This visual indicates a secondary confirmation where Parabolic SAR and MACD align with the main trend direction. It serves as an additional confluence factor to increase confidence in the trade setup.
Support/Resistance Table
A small table located at the bottom-right of the chart.
• Function: Automatically identifies and displays recent Pivot Highs (Resistance) and Pivot Lows (Support).
• Interpretation: These levels can be used as potential targets for Take Profit or invalidation points for manual Stop Loss adjustments.
🖥️ Chapter 5: Dashboard & Operational Guide
Integrated Analytics Panel (Dashboard Overview)
To facilitate rapid decision-making without manual calculation, the system aggregates critical market dimensions into a unified "Heads-Up Display" (HUD). This panel monitors real-time metrics across multiple timeframes and analytical vectors.
A. Intermediate Structure (12H Trend)
• Function: Anchors the intraday analysis to the broader market structure using a 12-hour rolling window.
• Interpretation:
◦ Bullish (> +0.5%): Indicates a positive structural bias. Long setups align with the macro flow.
◦ Bearish (< -0.5%): Indicates structural weakness. Short setups are statistically favored.
◦ Neutral: Represents a ranging environment where the Confluence Score becomes the primary weighting factor.
B. Composite Confluence Score (Signal Confidence)
• Definition: A probability metric derived from the synchronization of Volatility (Impulse Core), Momentum (Ribbon), and Trend (Baseline).
• Grading Scale:
Strong Buy/Sell (> 7.0 / < 3.0): Indicates full alignment across all three vectors. Represents a "Prime Setup" eligible for standard position sizing.
Buy/Sell (5.0–7.0 / 3.0–5.0): Indicates a valid trend but with moderate volatility confirmation.
Neutral: Signals conflicting data (e.g., Bullish Momentum vs. Bearish Structure). Trading is not recommended ("No-Trade Zone").
C. Statistical Deviation Status (Mean Reversion)
• Logic: Utilizes Bollinger Band deviation principles to quantify how far price has stretched from the statistical mean (20 SMA).
• Alert States:
Over-Extended (> 2.0 SD): Warning that price is statistically likely to revert to the mean (Elastic Snap-back), even if the trend remains technically valid. New entries are discouraged in this zone.
Normal: Price is within standard distribution limits, suitable for trend-following entries.
D. Volatility Regime Classification
• Metric: Compares current ATR against a 100-period historical baseline to categorize the market state.
• Regimes:
Low Volatility (Lvl < 1.0): Market Compression. Often precedes volatility expansion events.
Mid Volatility (Lvl 1.0 - 1.5): Standard operating environment.
High Volatility (Lvl > 1.5): Elevated market stress. Risk parameters should be adjusted (e.g., reduced position size) to account for increased variance.
E. Performance Telemetry
• Function: Displays the historical reliability of the Trend Baseline for the current asset and timeframe.
• Operational Threshold: If the displayed Win Rate falls below 40%, it suggests the current market behavior is incoherent (choppy) and does not respect trend logic. In such cases, switching assets or timeframes is recommended.
Operational Protocols & Signal Decoding
Visual Interpretation Standards
• Laminar Flow (Trade Confirmation): A valid trend is visually confirmed when the 15-layer SMA Ribbon is fully expanded and parallel. This indicates distributed momentum across timeframes.
• Consolidation (No-Trade): If the ribbon appears twisted, knotted, or compressed, the market lacks a unified directional vector.
• Baseline Interaction: The Triple-Smoothed Baseline acts as a dynamic support/resistance filter. Long positions remain valid only while price sustains above this structure.
System Calibration (Settings)
• Adaptive Signal Filtering (Prev. Anti-Greed): Enabled by default. This logic automatically raises the required trend slope threshold following consecutive wins to mitigate behavioral bias.
• Impulse Sensitivity: Controls the reactivity of the Volatility Core. Higher settings capture faster moves but may introduce more noise.
⚙️ Chapter 6: System Configuration & Alert Guide
This section provides a complete breakdown of every adjustable setting within Impulse Reactor to assist you in tailoring the engine to your specific needs.
🌐 LANGUAGE SETTINGS (Localization)
◦ Select Language (Default: English):
Function: Instantly translates all chart labels, dashboard texts into your preferred language.
Supported: English, Korean, Chinese, Spanish
⚡ IMPULSE CORE SETTINGS (Volatility Engine)
◦ Deviation Lookback (Default: 30): The period used to calculate the standard deviation of volatility.
Role: Sets the baseline for normalizing momentum. Higher values make the core smoother but slower to react.
◦ Fast Pulse Length (Default: 10): The short-term ATR period.
Role: Detects rapid volatility expansion.
◦ Slow Pulse Length (Default: 30): The long-term ATR baseline.
Role: Establishes the background volatility level. The core signal is derived from the divergence between Fast and Slow pulses.
🎯 TP/SL SETTINGS (Risk Management)
◦ SL/TP Fibonacci (Default: 0.786 / 1.618): Selects the Fibonacci ratio used for risk calculation.
◦ SL/TP Multiplier (Default: 1.5 / 2): Applies a multiplier to the ATR-based bands.
Role: Expands or contracts the Take Profit and Stop Loss boxes. Increase these values for higher volatility assets (like Altcoins) to avoid premature stop-outs.
◦ ATR Length (Default: 14): The lookback period for calculating the Average True Range used in risk geometry.
◦ Use Soft Stop (Close Basis):
Role: If enabled, Stop Loss alerts only trigger if a candle closes beyond the invalidation level. This prevents being stopped out by wick manipulations.
🔊 RIBBON SETTINGS (Momentum Visualization)
◦ Show SMA Ribbon: Toggles the visibility of the 15-layer gradient ribbon.
◦ Ribbon Line Count (Default: 15): The number of SMA lines in the ribbon array.
◦ Ribbon Start Length (Default: 2) & Step (Default: 1): Defines the spread of the ribbon.
Role: Controls the "thickness" of the momentum density visualization. A wider step creates a broader ribbon, useful for higher timeframes.
📎 DISPLAY OPTIONS
◦ Show Entry Lines / TP/SL Box / Position Labels / S/R Levels / Dashboard: Toggles individual visual elements on the chart to reduce clutter.
◦ Show SAR+MACD Glow: Enables the secondary confirmation shapes (triangles/circles) above/below candles.
📈 TREND BASELINE (Structural Filter)
◦ Supertrend Factor (Default: 12) & ATR Period (Default: 90): Controls the sensitivity of the underlying Supertrend algorithm used for the baseline calculation.
◦ WMA Length (40) & EMA Length (14): The smoothing periods for the Triple-Smoothed Baseline.
◦ Min Trend Duration (Default: 10): The minimum number of bars the trend must be established before a signal is considered valid.
🧠 SMART EXIT (Dynamic Liquidity)
◦ Use Smart Exit: Enables the momentum exhaustion logic.
◦ Exit Threshold Score (Default: 3): The sensitivity level for triggering a Smart Exit. Lower values trigger earlier exits.
◦ Average Period (168) & Min Hold Bars (5): Defines the rolling window for momentum decay analysis and the minimum duration a trade must be held before Smart Exit logic activates.
🛡️ TRAILING STOP (Step)
◦ Use Trailing Stop: Activates the step-function trailing mechanism.
◦ Step 1 Activation % (0.5) & Offset % (0.5): The price must move 0.5% in your favor to arm the first trail level, which sets a stop 0.5% behind price.
◦ Step 2 Activation % (1) & Offset % (0.2): Once price moves 1%, the trail tightens to 0.2%, securing the position.
🌀 SAR & MACD SETTINGS (Secondary Confirmation)
◦ SAR Start/Increment/Max: Standard Parabolic SAR parameters.
◦ SAR Score Scaling (ATR): Adjusts how much weight the SAR signal has in the overall confluence score.
◦ MACD Fast/Slow/Signal: Standard MACD parameters used for the "Glow" signals.
🔄 ANTI-GREED LOGIC (Behavioral Bias)
◦ Strict Entry after Win: Enables the negative feedback loop.
◦ Strict Multiplier (Default: 1.1): Increases the entry difficulty by 10% after each win.
Role: Prevents overtrading and entering at the top of an extended trend.
🌍 HTF FILTER (Multi-Timeframe)
◦ Use Auto-Adaptive HTF Filter: Automatically selects a higher timeframe (e.g., 1H -> 4H) to filter signals.
◦ Bypass HTF on Steep Trigger: Allows an entry even against the HTF trend if the local momentum slope is exceptionally steep (catch powerful reversals).
📉 RSI PEAK & CHOPPINESS
◦ RSI Peak Exit (Instant): Triggers an immediate exit if a sharp RSI pivot (V-shape) is detected.
◦ Choppiness Filter: Suppresses signals if the Choppiness Index is above the threshold (Default: 60), indicating a flat market.
📐 SLOPE TRIGGER LOGIC
◦ Force Entry on Steep Slope: Overrides other filters if the price angle is extremely vertical (high velocity).
◦ Slope Sensitivity (1.5): The angle required to trigger this override.
⛔ FLAT MARKET FILTER (ADX & ATR)
◦ Use ADX Filter: Blocks signals if ADX is below the threshold (Default: 20), indicating no trend.
◦ Use ATR Flat Filter: Blocks signals if volatility drops below a critical level (dead market).
🔔 Alert Configuration Guide
Impulse Reactor is designed with a comprehensive suite of alert conditions, allowing you to automate your trading or receive real-time notifications for specific market events.
How to Set Up:
Click the "Alert" (Clock) icon in the TradingView toolbar.
Select "Impulse Reactor " from the Condition dropdown.
Choose one of the specific trigger conditions below:
🚀 Entry Signals (Trend Initiation)
Long Entry:
Trigger: Fires when a confirmed Bullish Setup is detected (Momentum + Volatility + Structure align).
Usage: Use this to enter new Long positions.
Short Entry:
Trigger: Fires when a confirmed Bearish Setup is detected.
Usage: Use this to enter new Short positions.
🎯 Profit Taking (Target Levels)
Long TP:
Trigger: Fires when price hits the calculated Take Profit level for a Long trade.
Usage: Automate partial or full profit taking.
Short TP:
Trigger: Fires when price hits the calculated Take Profit level for a Short trade.
Usage: Automate partial or full profit taking.
🛡️ Defensive Exits (Risk Management)
Smart Exit:
Trigger: Fires when the system detects momentum decay or statistical exhaustion (even if the trend hasn't fully reversed).
Usage: Recommended for tightening stops or closing positions early to preserve gains.
Overbought / Oversold:
Trigger: Fires when the ribbon extends into extreme zones.
Usage: Warning signal to prepare for a potential reversal or pullback.
💡 Secondary Confirmation (Confluence)
SAR+MACD Bullish:
Trigger: Fires when Parabolic SAR and MACD align bullishly with the main trend.
Usage: Ideal for Pyramiding (adding to an existing winning position).
SAR+MACD Bearish:
Trigger: Fires when Parabolic SAR and MACD align bearishly.
Usage: Ideal for adding to short positions.
⚠️ Chapter 7: Conclusion & Risk Disclosure
Methodological Synthesis
Impulse Reactor represents a shift from reactive price tracking to proactive energy analysis. By decomposing market activity into its atomic components — Volatility, Momentum, and Structure — and reconstructing them into a coherent decision model, the system aims to provide a quantitative framework for market engagement. It is designed not to predict the future, but to identify high-probability conditions where kinetic energy and trend structure align.
Disclaimer & Risk Warnings
◦ Educational Purpose Only
This indicator, including all associated code, documentation, and visual outputs, is provided strictly for educational and informational purposes. It does not constitute financial advice, investment recommendations, or a solicitation to buy or sell any financial instruments.
◦ No Guarantee of Performance
Past performance is not indicative of future results. All metrics displayed on the dashboard (including "Win Rate" and "P&L") are theoretical calculations based on historical data. These figures do not account for real-world trading factors such as slippage, liquidity gaps, spread costs, or broker commissions.
◦ High-Risk Warning
Trading cryptocurrencies, futures, and leveraged financial products involves a substantial risk of loss. The use of leverage can amplify both gains and losses. Users acknowledge that they are solely responsible for their trading decisions and should conduct independent due diligence before executing any trades.
◦ Software Limitations
The software is provided "as is" without warranty. Users should be aware that market data feeds on analysis platforms may experience latency or outages, which can affect signal generation accuracy.
Interactive Compound Interest ProjectorThis indicator is an interactive tool designed for long-term investors and analysts who want to compare an asset's performance against a theoretical compound interest growth curve.
Unlike static tools, this script utilizes the Interactive Anchor feature. This allows you to click on any specific point on the chart (e.g., a market bottom, a specific entry date, or a previous all-time high) to serve as the starting point ("Principal") for the projection.
How to use
Add the indicator to your chart.
Important: Because confirm=true is enabled, the script will wait for you to click on the chart. Click on the specific candle you want to use as the "Start Date".
The Yellow Line will appear starting from that candle.
Open the indicator settings to adjust:
Annual Interest Rate: (Default 6.0%).
Project until Year: (Default 2050).
Use this to visualize if an asset is "beating" a standard benchmark (like a 10% S&P500 average or a 4% risk-free rate) from a specific moment in time.
Disclaimer: This tool is for educational and comparative analysis purposes only and does not guarantee future results.
Meet The Neural Brain: The "Glass Box" AnalystIt observes. It thinks. It speaks.
Most indicators are "Black Boxes"—they give you a signal, but they never tell you why. If you don't know the why, you can't trust the trade.
The Neural Brain is different. It is a "Glass Box" AI Market Analyst that lives on your chart. It breaks down its decision-making process into plain English, so you never have to guess.
How It "Thinks" (The 3-Layer Cortex)
It processes market data through three human-like layers:
1. PERCEPTION (The Eyes)
Forensic Analysis: It scans price action for "clean" vs. "noisy" movement.
Spread History: It tracks momentum expansion in real-time.
2. COGNITION (The Mind)
Mode Selection: It mathematically decides whether to TRACK a trend or REPEL choppy conditions.
Conviction Monitor: It tells you if its confidence in the trade is growing or fading.
3. NARRATIVE (The Voice)
The Killer Feature: It synthesizes all data into a final strategic summary displayed right on your screen:
"STRONG TREND + NOISY ACTION = HOLDING (Ignoring Noise)"
"Is this Real AI?"
Transparency is our priority. This is not "Generative AI" (like ChatGPT) that hallucinates or scours the internet. This is Expert Systems Machine Learning.
The Math: We utilize the Rational Quadratic Kernel, a sophisticated technique used in Gaussian Process Regression. This allows the code to "learn" the structure of volatility without being explicitly hard-coded. It adapts to the market curve.
The Logic: It mimics the thought process of a professional trader running a complex Decision Tree:
Human Thought: "The trend is up, but it's choppy, so I should wait."
Neural Logic: IF (Trend > 0) AND (Efficiency < 0.3) THEN Strategy = "HOLDING"
The Result: A Synthetic Cortex that adapts to the situation just like a pro trader would, giving you the clarity to execute with zero hesitation.
Linear Moments█ OVERVIEW
The Linear Moments indicator, also known as L-moments, is a statistical tool used to estimate the properties of a probability distribution. It is an alternative to conventional moments and is more robust to outliers and extreme values.
█ CONCEPTS
█ Four moments of a distribution
We have mentioned the concept of the Moments of a distribution in one of our previous posts. The method of Linear Moments allows us to calculate more robust measures that describe the shape features of a distribution and are anallougous to those of conventional moments. L-moments therefore provide estimates of the location, scale, skewness, and kurtosis of a probability distribution.
The first L-moment, λ₁, is equivalent to the sample mean and represents the location of the distribution. The second L-moment, λ₂, is a measure of the dispersion of the distribution, similar to the sample standard deviation. The third and fourth L-moments, λ₃ and λ₄, respectively, are the measures of skewness and kurtosis of the distribution. Higher order L-moments can also be calculated to provide more detailed information about the shape of the distribution.
One advantage of using L-moments over conventional moments is that they are less affected by outliers and extreme values. This is because L-moments are based on order statistics, which are more resistant to the influence of outliers. By contrast, conventional moments are based on the deviations of each data point from the sample mean, and outliers can have a disproportionate effect on these deviations, leading to skewed or biased estimates of the distribution parameters.
█ Order Statistics
L-moments are statistical measures that are based on linear combinations of order statistics, which are the sorted values in a dataset. This approach makes L-moments more resistant to the influence of outliers and extreme values. However, the computation of L-moments requires sorting the order statistics, which can lead to a higher computational complexity.
To address this issue, we have implemented an Online Sorting Algorithm that efficiently obtains the sorted dataset of order statistics, reducing the time complexity of the indicator. The Online Sorting Algorithm is an efficient method for sorting large datasets that can be updated incrementally, making it well-suited for use in trading applications where data is often streamed in real-time. By using this algorithm to compute L-moments, we can obtain robust estimates of distribution parameters while minimizing the computational resources required.
█ Bias and efficiency of an estimator
One of the key advantages of L-moments over conventional moments is that they approach their asymptotic normal closer than conventional moments. This means that as the sample size increases, the L-moments provide more accurate estimates of the distribution parameters.
Asymptotic normality is a statistical property that describes the behavior of an estimator as the sample size increases. As the sample size gets larger, the distribution of the estimator approaches a normal distribution, which is a bell-shaped curve. The mean and variance of the estimator are also related to the true mean and variance of the population, and these relationships become more accurate as the sample size increases.
The concept of asymptotic normality is important because it allows us to make inferences about the population based on the properties of the sample. If an estimator is asymptotically normal, we can use the properties of the normal distribution to calculate the probability of observing a particular value of the estimator, given the sample size and other relevant parameters.
In the case of L-moments, the fact that they approach their asymptotic normal more closely than conventional moments means that they provide more accurate estimates of the distribution parameters as the sample size increases. This is especially useful in situations where the sample size is small, such as when working with financial data. By using L-moments to estimate the properties of a distribution, traders can make more informed decisions about their investments and manage their risk more effectively.
Below we can see the empirical dsitributions of the Variance and L-scale estimators. We ran 10000 simulations with a sample size of 100. Here we can clearly see how the L-moment estimator approaches the normal distribution more closely and how such an estimator can be more representative of the underlying population.
█ WAYS TO USE THIS INDICATOR
The Linear Moments indicator can be used to estimate the L-moments of a dataset and provide insights into the underlying probability distribution. By analyzing the L-moments, traders can make inferences about the shape of the distribution, such as whether it is symmetric or skewed, and the degree of its spread and peakedness. This information can be useful in predicting future market movements and developing trading strategies.
One can also compare the L-moments of the dataset at hand with the L-moments of certain commonly used probability distributions. Finance is especially known for the use of certain fat tailed distributions such as Laplace or Student-t. We have built in the theoretical values of L-kurtosis for certain common distributions. In this way a person can compare our observed L-kurtosis with the one of the selected theoretical distribution.
█ FEATURES
Source Settings
Source - Select the source you wish the indicator to calculate on
Source Selection - Selec whether you wish to calculate on the source value or its log return
Moments Settings
Moments Selection - Select the L-moment you wish to be displayed
Lookback - Determine the sample size you wish the L-moments to be calculated with
Theoretical Distribution - This setting is only for investingating the kurtosis of our dataset. One can compare our observed kurtosis with the kurtosis of a selected theoretical distribution.
Relative Strength Heatmap [BackQuant]Relative Strength Heatmap
A multi-horizon RSI matrix that compresses 20 different lookbacks into a single panel, turning raw momentum into a visual “pressure gauge” for overbought and oversold clustering, trend exhaustion, and breadth of participation across time horizons.
What this is
This indicator builds a strip-style heatmap of 20 RSIs, each with a different length, and stacks them vertically as colored tiles in a single pane. Every tile is colored by its RSI value using your chosen palette, so you can see at a glance:
How many “fast” versus “slow” RSIs are overbought or oversold.
Whether momentum is concentrated in the short lookbacks or spread across the whole curve.
When momentum extremes cluster, signalling strong market pressure or exhaustion.
On top of the tiles, the script plots two simple breadth lines:
A white line that counts how many RSIs are above 70 (overbought cluster).
A black line that counts how many RSIs are below 30 (oversold cluster).
This turns a single symbol’s RSI ladder into a compact “market pressure gauge” that shows not only whether RSI is overbought or oversold, but how many different horizons agree at the same time.
Core idea
A single RSI looks at one length and one timescale. Markets, however, are driven by flows that operate on multiple horizons at once. By computing RSI over a ladder of lengths, you approximate a “term structure” of strength:
Short lengths react to immediate swings and very recent impulses.
Medium lengths reflect swing behaviour and local trends.
Long lengths reflect structural bias and higher timeframe regime.
When many lengths agree, for example 10 or more RSIs all above 70, it suggests broad participation and strong directional pressure. When only a few fast lengths stretch to extremes while longer ones stay neutral, the move is more fragile and more likely to mean-revert.
This script makes that structure visible as a heatmap instead of forcing you to run many separate RSI panes.
How it works
1) Generating RSI lengths
You control three parameters in the calculation settings:
RS Period – the base RSI length used for the shortest strip.
RSI Step – the amount added to each successive RSI length.
RSI Multiplier – a global scaling factor applied after the step.
Each of the 20 RSIs uses:
RSI length = round((base_length + step × index) × multiplier) , where the index goes from 0 to 19.
That means:
RSI 1 uses (len + step × 0) × mult.
RSI 2 uses (len + step × 1) × mult.
…
RSI 20 uses (len + step × 19) × mult.
You can keep the ladder dense (small step and multiplier) or stretch it across much longer horizons.
2) Heatmap layout and grouping
Each RSI is plotted as an “area” strip at a fixed vertical level using histbase to stack them:
RSI 1–5 form Group 1.
RSI 6–10 form Group 2.
RSI 11–15 form Group 3.
RSI 16–20 form Group 4.
Each group has a toggle:
Show only Group 1 and 2 if you care mainly about fast and medium horizons.
Show all groups for a full spectrum from very short to very long.
Hide any group that feels redundant for your workflow.
The actual numeric RSI values are not plotted as lines. Instead, each strip is drawn as a horizontal band whose fill color represents the current RSI regime.
3) Palette-based coloring
Each tile’s color is driven by the RSI value and your chosen palette. The script includes several palettes:
Viridis – smooth green to yellow, good for subtle reading.
Jet – strong blue to red sequence with high contrast.
Plasma – purple through orange to yellow.
Custom Heat – cool blues to neutral grey to hot reds.
Gray – grayscale from white to black for minimalistic layouts.
Cividis, Inferno, Magma, Turbo, Rainbow – additional scientific and rainbow-style maps.
Internally, RSI values are bucketed into ranges (for example, below 10, 10–20, …, 90–100). Each bucket maps to a unique colour for that palette. In all schemes, low RSI values are mapped to the “cold” or darker side and high RSI values to the “hot” or brighter side.
The result is a true momentum heatmap:
Cold or dark tiles show low RSI and oversold or compressed conditions.
Mid tones show neutral or mid-range RSI.
Warm or bright tiles show high RSI and overbought or stretched conditions.
4) Bull and bear breadth counts
All 20 RSI values are collected into an array each bar. Two counters are then calculated:
Bull count – how many RSIs are above 70.
Bear count – how many RSIs are below 30.
These are plotted as:
A white line (“RSI > 70 Count”) for the overbought cluster.
A black line (“RSI < 30 Count”) for the oversold cluster.
If you enable the “Show Bull and Bear Count” option, you get an immediate reading of how many of the 20 horizons are stretched at any moment.
5) Cluster alerts and background tagging
Two alert conditions monitor “strong cluster” regimes:
RSI Heatmap Strong Bull – triggers when at least 10 RSIs are above 70.
RSI Heatmap Strong Bear – triggers when at least 10 RSIs are below 30.
When one of these conditions is true, the indicator can tint the background of the chart using a soft version of the current palette. This visually marks stretches where momentum is extreme across many lengths at once, not just on a single RSI.
What it plots
In one oscillator window, the indicator provides:
Up to 20 horizontal RSI strips, each representing a different RSI length.
Color-coded tiles reflecting the current RSI value for each length.
Group toggles to show or hide each block of five RSIs.
An optional white line that counts how many RSIs are above 70.
An optional black line that counts how many RSIs are below 30.
Optional background highlights when the number of overbought or oversold RSIs passes the strong-cluster threshold.
How it measures breadth and pressure
Single-symbol breadth
Breadth is usually defined across a basket of symbols, such as how many stocks advance versus decline. This indicator uses the same concept across time horizons for a single symbol. The question becomes:
“How many different RSI lengths are stretched in the same direction at once?”
Examples:
If only 2 or 3 of the shortest RSIs are above 70, bull count stays low. The move is fast and local, but not yet broadly supported.
If 12 or more RSIs across short, medium and long lengths are above 70, the bull count spikes. The move has broad momentum and strong upside pressure.
If 10 or more RSIs are below 30, bear count spikes and you are in a broad oversold regime.
This is breadth of momentum within one market.
Market pressure gauge
The combination of heatmap tiles and breadth lines acts as a pressure gauge:
High bull count with warm colors across most strips indicates strong upside pressure and crowded long positioning.
High bear count with cold colors across most strips indicates strong downside pressure and capitulation or forced selling.
Low counts with a mixed heatmap indicate neutral pressure, fragmented flows, or range-bound conditions.
You can treat the strong-cluster alerts as “extreme pressure” signals. When they fire, the market is heavily skewed in one direction across many horizons.
How to read the heatmap
Horizontal patterns (through time)
Look along the time axis and watch how the colors evolve:
Persistent hot tiles across many strips show sustained bullish pressure and trend strength.
Persistent cold tiles across many strips show sustained bearish pressure and weak demand.
Frequent flipping between hot and cold colours indicates a choppy or mean-reverting environment.
Vertical structure (across lengths at one bar)
Focus on a single bar and read the column of tiles from top to bottom:
Short RSIs hot, long RSIs neutral or cool: early trend or short-term fomo. Price has moved fast, longer horizons have not caught up.
Short and long RSIs all hot: mature, entrenched uptrend. Broad participation, high pressure, greater risk of blow-off or late-entry vulnerability.
Short RSIs cold but long RSIs mid to high: pullback in a higher timeframe uptrend. Dip-buy and continuation setups are often found here.
Short RSIs high but long RSIs low: countertrend rallies within a broader downtrend. Good hunting ground for fades and short entries after a bounce.
Bull and bear breadth lines
Use the two lines as simple, numeric breadth indicators:
A rising white line shows more RSIs pushing above 70, so bullish pressure is expanding in breadth.
A rising black line shows more RSIs pushing below 30, so bearish pressure is expanding in breadth.
When both lines are low and flat, few horizons are extreme and the market is in mid-range territory.
Cluster zones
When either count crosses the strong threshold (for example 10 out of 20 RSIs in extreme territory):
A strong bull cluster marks a broadly overbought regime. Trend followers may see this as confirmation. Mean-reversion traders may see it as a late-stage or blow-off context.
A strong bear cluster marks a broadly oversold regime. Downtrend traders see strong pressure, but the risk of sharp short-covering bounces also increases.
Trading applications
Trend confirmation
Use the heatmap and breadth lines as a trend filter:
Prefer long setups when the heatmap shows mostly mid to high RSIs and the bull count is rising.
Avoid fresh shorts when there is a strong bull cluster, unless you are specifically trading exhaustion.
Prefer short setups when the heatmap is mostly low RSIs and the bear count is rising.
Avoid aggressive longs when a strong bear cluster is active, unless you are trading reflexive bounces.
Mean-reversion timing
Treat cluster extremes as exhaustion zones:
Look for reversal patterns, failed breakouts, or order flow shifts when bull count is very high and price starts to stall or diverge.
Look for reflexive bounce potential when bear count is very high and price stops making new lows or shows absorption at the lows.
Use the palette and counts together: hot tiles plus a peaking white line can mark blow-off conditions, cold tiles plus a peaking black line can mark capitulation.
Regime detection and risk toggling
Use the overall shape of the ladder over time:
If upper strips stay warm and lower strips stay neutral or warm for extended periods, the market is in an uptrend regime. You can justify higher risk for long-biased strategies.
If upper strips stay cold and lower strips stay neutral or cold, the market is in a downtrend regime. You can justify higher risk for short-biased strategies or defensive positioning.
If colours and counts flip frequently, you are likely in a range or choppy regime. Consider reducing size or using more tactical, short-term strategies.
Multi-horizon synchronization
You can think of each RSI length as a proxy for a different “speed” of the same market:
When only fast RSIs are stretched, the move is local and less robust.
When fast, medium and slow RSIs align, the move has multi-horizon confirmation.
You can require a minimum bull or bear count before allowing your main strategy to engage.
Spotting hidden shifts
Sometimes price appears flat or drifting, but the heatmap quietly cools or warms:
If price is sideways while many hot tiles fade toward neutral, momentum is decaying under the surface and trend risk is increasing.
If price is sideways while many cold tiles climb back toward neutral, selling pressure is decaying and the tape is repairing itself.
Settings overview
Calculation Settings
RS Period – base RSI length for the shortest strip.
RSI Step – the increment added to each successive RSI length.
RSI Multiplier – scales all generated RSI lengths.
Calculation Source – the input series, such as close, hlc3 or others.
Plotting and Coloring Settings
Heatmap Color Palette – choose between Viridis, Jet, Plasma, Custom Heat, Gray, Cividis, Inferno, Magma, Turbo or Rainbow.
Show Group 1 – toggles RSI 1–5.
Show Group 2 – toggles RSI 6–10.
Show Group 3 – toggles RSI 11–15.
Show Group 4 – toggles RSI 16–20.
Show Bull and Bear Count – enables or disables the two breadth lines.
Alerts
RSI Heatmap Strong Bull – fires when the number of RSIs above 70 reaches or exceeds the configured threshold (default 10).
RSI Heatmap Strong Bear – fires when the number of RSIs below 30 reaches or exceeds the configured threshold (default 10).
Tuning guidance
Fast, tactical configurations
Use a small base RS Period, for example 2 to 5.
Use a small RSI Step, for tight clustering around the fast horizon.
Keep the multiplier near 1.0 to avoid extreme long lengths.
Focus on Group 1 and Group 2 for intraday and short-term trading.
Swing and position configurations
Use a mid-range RS Period, for example 7 to 14.
Use a moderate RSI Step to fan out into slower horizons.
Optionally use a multiplier slightly above 1.0.
Keep all four groups enabled for a full view from fast to slow.
Macro or higher timeframe configurations
Use a larger base RS Period.
Use a larger RSI Step so the top of the ladder reaches very slow lengths.
Focus on Group 3 and Group 4 to see structural momentum.
Treat clusters as regime markers rather than frequent trading signals.
Notes
This indicator is a contextual tool, not a standalone trading system. It does not model execution, spreads, slippage or fundamental drivers. Use it to:
Understand whether momentum is narrow or broad across horizons.
Confirm or filter existing signals from your primary strategy.
Identify environments where the market is crowded into one side.
Distinguish between isolated spikes and truly broad pressure moves.
The Relative Strength Heatmap is designed to answer a simple but powerful question:
“How many versions of RSI agree with what I am seeing on the chart?”
By compressing those answers into a single panel with clear colour coding and breadth lines, it becomes a practical, visual gauge of momentum breadth and market pressure that you can overlay on any trading framework.
Filter Volume1. Indicator Name
Filter Volume
2. One-line Introduction
A regression-based trend filter that quantifies and visualizes market direction and strength using price behavior.
3. Overall Description
Filter Volume+ is a trend-detection indicator that uses linear regression to evaluate the dominant direction of price movement over a given period.
It compares historical regression values to determine whether the market is in a bullish, bearish, or neutral state.
The indicator applies a percentage threshold to filter out weak or indecisive trends, highlighting only significant movements.
Each trend state is visualized through distinct colors: bullish (greenish), bearish (reddish), and neutral (gray), with intensity reflecting trend strength.
To reduce noise and create smooth visual signals, a three-step smoothing process is applied to the raw trend intensity.
Users can customize the regression source, lookback period, and sensitivity, allowing the indicator to adapt to various assets and timeframes.
This tool is especially useful in filtering entry signals based on clear directional bias, making it suitable for trend-following or confirmation strategies.
4. Key Benefits (Title + Description)
✅ Quantified Trend Strength
Only displays trend signals when a statistically significant direction is detected using linear regression comparisons.
✅ Visual Clarity with Color Coding
Each market state (bullish, bearish, neutral) is represented with distinct colors and transparency, enabling fast interpretation.
✅ Custom Regression Source
Users can define the data input (e.g., close, open, indicator output) for regression calculation, increasing strategic flexibility.
✅ Multi-Level Smoothing
Applies three layers of smoothing (via moving averages) to eliminate noise and produce a stable, flowing trend curve.
✅ Area Fill Visualization
Plots a colored band between the trend value and zero-line, helping users quickly gauge the market's dominant force.
✅ Adjustable Sensitivity Settings
Includes tolerance and lookback controls, allowing traders to fine-tune how reactive or conservative the trend detection should be.
5. Indicator User Guide
📌 Basic Concept
Filter Volume+ assesses the direction of price by comparing regression values over a selected period.
If the percentage of upward comparisons exceeds a threshold, a bullish state is shown; if downward comparisons dominate, it shows a bearish state.
⚙️ Settings Overview
Lookback Period (n): The number of bars to compare for trend analysis
Range Tolerance (%): Minimum threshold for declaring a strong trend
Regression Source: The data used for regression (e.g., close, open)
Linear Regression Length: Number of bars used to compute each regression value
Bull/Bear Color: Custom colors for bullish and bearish trends
📈 Example Timing
When the trend line stays above zero and the green color intensity increases → trend gaining strength
After a neutral phase (gray), the color shifts quickly to greenish → early trend reversal
📉 Example Timing
When the trend line stays below zero with deepening red color → strong bearish continuation
Sudden change from bullish to bearish color with rising intensity
🧪 Recommended Use
Use as a trend confirmation filter alongside entry/exit strategies
Ideal for swing or position trades in trending markets
Combine with oscillators like RSI or MACD for improved signal validation
🔒 Cautions
In ranging (sideways) markets, the color may change frequently – avoid relying solely on this indicator in those zones.
Low-intensity colors (faded) suggest weak trends – better to stay on the sidelines.
A short lookback period may cause over-sensitivity and false signals.
When using non-price regression sources, expect the indicator to behave differently – test before deploying.
+++
Market Breadth - [JTCAPITAL]Market Breadth - is a comprehensive crypto market strength and sentiment indicator designed to visualize the overall bullish or bearish alignment across 40 major cryptocurrencies. By combining multi-asset Exponential Moving Average (EMA) comparisons and smoothing techniques, it offers a clean, aggregated view of the broader market trend—helping traders quickly assess whether the market is dominated by bullish momentum or bearish pressure.
The indicator works by calculating in the following steps:
Symbol Selection and Data Retrieval
The script monitors 40 leading cryptocurrencies based on Market Cap. Each asset’s daily close price is requested using a 1D timeframe. This ensures that every data point reflects the same temporal resolution, allowing the indicator to evaluate global crypto strength rather than individual token volatility.
EMA Comparison per Asset
For each asset, two Exponential Moving Averages (EMAs) are calculated:
A short-term EMA with period emalength (default 10).
A long-term EMA with period emalength2 (default 20).
Each coin receives a score of +1 when the short-term EMA is greater than the long-term EMA (indicating bullish structure), or -1 when it is below (indicating bearish structure). This binary scoring system effectively converts individual price action into a directional sentiment measure.
Market Breadth Aggregation
All 40 individual scores are summed into a single composite value called scores .
If many assets have bullish EMA alignment, the total score becomes strongly positive.
If the majority show bearish alignment, the total score turns negative.
This step transforms scattered price data into one unified market breadth metric—quantifying how many assets participate in the same directional trend.
Smoothing the Breadth Line
To reduce short-term noise and isolate trend direction, the aggregated score is smoothed using an EMA of length = smoothlen (default 15). The resulting smoothed line helps identify sustained shifts in collective sentiment rather than temporary fluctuations.
Visualization and Color Coding
When scores > 0 , the market breadth is bullish and the histogram is colored blue.
When scores < 0 , the breadth turns bearish and the histogram is purple.
The same logic applies to the smoothed line and background color, offering an instant visual cue of market mood transitions.
Buy and Sell Conditions:
The indicator itself does not trigger direct buy/sell signals but rather acts as a market regime filter . Traders can use it as follows:
Buy Filter: When the smoothed value is above zero and rising, the majority of assets confirm an uptrend — this favors long setups or trend continuation entries.
Sell Filter: When the smoothed value is below zero and falling, bearish alignment dominates — ideal for short setups or defensive risk management.
Optional filters could include combining this with RSI or volume-weighted momentum indicators to confirm breadth-based reversals.
Features and Parameters:
emalength – Defines the short-term EMA length used for individual asset trend detection (default 10).
emalength2 – Defines the long-term EMA length (default 20).
smoothlen – Defines the smoothing EMA length for the total market breadth line (default 15).
40 asset inputs – User-editable symbols allow full customization of which cryptos are tracked.
Dynamic color backgrounds – Visual distinction between bullish and bearish phases.
Specifications:
Exponential Moving Average (EMA)
EMA is a type of moving average that places more weight on recent price data, responding faster to market changes compared to SMA. By comparing a short-term and long-term EMA, the indicator captures momentum shifts across each asset individually. The crossover logic (EMA10 > EMA20) signals bullish conditions, while the opposite indicates bearish momentum.
Market Breadth
Market Breadth quantifies how many assets are participating in a directional move. Instead of tracking a single coin’s trend, breadth analysis measures collective sentiment. When most coins’ short-term EMAs are above long-term EMAs, the market shows healthy bullish breadth. Conversely, when most are below, weakness dominates.
Smoothing (EMA on Scores)
After summing the breadth score, the result is smoothed with an additional EMA to mitigate the inherent volatility caused by individual coin reversals. This second-level smoothing transforms raw fluctuations into a readable, trend-consistent curve.
Color Visualization
Visual cues are integral for intuitive interpretation.
Blue Shades: Indicate bullish alignment and collective upward momentum.
Purple Shades: Indicate bearish conditions and potential risk-off phases.
The background tint reinforces visual clarity even when the indicator is overlaid on price charts.
Background Logic
By applying the same color logic to the chart’s background, users can instantly recognize the prevailing market phase.
Use Cases
As a trend confirmation filter for other indicators (e.g., trade only in the direction of positive breadth).
As a divergence tool : when price rises but breadth weakens, it may signal a topping market.
As a macro sentiment monitor : perfect for assessing when the crypto market as a whole transitions from bearish to bullish structure.
Summary
“ Market Breadth - ” transforms the chaotic price movements of 40 cryptocurrencies into a single, powerful visual representation of overall market health. By merging EMA cross analysis with market-wide aggregation and smoothing , it provides traders with a deep understanding of when bullish or bearish forces dominate the ecosystem.
It’s a clean, data-driven approach to identifying shifts in crypto market sentiment — a perfect companion for trend-following, macro analysis, and timing portfolio exposure.
Enjoy!
Alpha-Weighted RSIDescription:
The Alpha-Weighted RSI is a next-generation momentum oscillator that redefines the classic RSI by incorporating the mathematical principles of Lévy Flight. This advanced adaptation applies non-linear weighting to price changes, making the indicator more sensitive to significant market moves and less reactive to minor noise. It is designed for traders seeking a clearer, more powerful view of momentum and potential reversal zones.
🔍 Key Features & Innovations:
Lévy Flight Alpha Weighting: At the core of this indicator is the Alpha parameter (1.0-2.0), which controls the sensitivity to price changes.
Lower Alpha (e.g., 1.2): Makes the indicator highly responsive to recent price movements, ideal for capturing early trend shifts.
Higher Alpha (e.g., 1.8): Creates a smoother, more conservative output that filters out noise, focusing on stronger momentum.
Customizable Smoothing: The raw Lévy-RSI is smoothed by a user-selectable moving average (8 MA types supported: SMA, EMA, SMMA, etc.), allowing for further customization of responsiveness.
Intuitive Centered Oscillator: The RSI is centered around a zero line, providing a clean visual separation between bullish and bearish territory.
Dynamic Gradient Zones: Subtle, colour coded gradient fills in the overbought (>+25) and oversold (<-25) regions enhance visual clarity without cluttering the chart.
Modern Histogram Display: Momentum is plotted as a sleek histogram that changes color between bright cyan (bullish) and magenta (bearish) based on its position relative to the zero line.
🎯 How to Use & Interpret:
Zero-Line Crossovers: The most basic signals. A crossover above the zero line indicates building bullish momentum, while a crossover below suggests growing bearish momentum.
Overbought/Oversold Levels: Use the +25/-25 and +35/-35 levels as dynamic zones. A reading above +25 suggests strong bullish momentum (overbought), while a reading below -25 indicates strong bearish momentum (oversold).
Divergence Detection: Look for divergences between the Alpha-Weighted RSI and price action. For example, if price makes a new low but the RSI forms a higher low, it can signal a potential bullish reversal.
Alpha Tuning: Adjust the Alpha parameter to match market volatility. In choppy markets, increase alpha to reduce noise. In trending markets, decrease alpha to become more responsive.
⚙️ Input Parameters:
RSI Settings: Standard RSI inputs for Length and Calculation Source.
Lévy Flight Settings: The crucial Alpha factor for response control.
MA Settings: MA Type and MA Length for smoothing the final output.
By applying Lévy Flight dynamics, this indicator offers a nuanced perspective on momentum, helping you stay ahead of the curve. Feedback is always welcome!
Better DEMAThe Better DEMA is a new tool designed to recreate the classical moving average DEMA, into a smoother, more reliable tool. Combining many methodologies, this script offers users a unique insight into market behavior.
How does it work?
First, to get a smoother signal, we need to calculate the Gaussian filter. A Gaussian filter is a smoothing filter that reduces noise and detail by averaging data with weights following a Gaussian (bell-shaped) curve.
Now that we have the source, we will calculate the following:
n2 = n/2 (half of the user defined length)
a = 2/(1+n)
ns
Now that we have that out of the way, it is time to get into the core.
Now we calculate 2 EMAs:
slow EMA => EMA over n
fast EMA => EMA over n2 period
Rather then now doing this:
DEMA = fast EMA * 2 - slow EMA
I found this to be better:
DEMA = slow EMA * (1-a) + fast EMA * a
As a last touch I took a little something from the HMA, and used a EMA with period of √n to smooth the entire the thing.
The Trend condition at base is the following (but feel free to FAFO with it):
Long = dema > dema yesterday and dema < src
Short = dema < dema yesterday and dema > src
Methodology
While the DEMA is an amazing tool used in many great indicators, it can be far too noisy.
This made me test out many filters, out of which the Gaussian performed best.
Then I tried out the non subtractive approach and that worked too, as it made it smoother.
Compacting on all I learned and smoothing it bit by bit, I think I can say this is worth looking into :).
Use cases:
Following Trends => classic, effective :)
Smoothing sources for other indicators => if done well enough, could be useful :)
Easy trend visualization => Added extra options for that.
Strategy development => Yes
Another good thing is it does not a high lookback period, so it should be better and less overfit.
That is all for today Gs,
Have fun and enjoy!
True Single Line Fusion [by TitikSona]🧠 Full Description
True Single Line Fusion by TitikSona is an open-source oscillator that unifies Fast Stochastic, Slow Stochastic, and RSI into a single smooth momentum line.
It simplifies multi-oscillator analysis into one clear visual — helping traders recognize potential momentum shifts, exhaustion, and reversal zones.
⚙️ Core Logic
The indicator calculates:
Fast Stochastic (12,3,3) → short-term swing sensitivity
Slow Stochastic (100,8,8) → broad trend context
RSI (26) → overall strength and directional bias
All three are normalized (0–100) and averaged to form the Fusion Line, creating a single unified momentum curve.
A Signal Line (SMA-9) and Histogram are added to highlight short-term acceleration or deceleration.
Formula: Fusion = (FastK + SlowK + RSI) / 3
🔍 Interpretation
Fusion Line rising → momentum strengthening upward
Fusion Line falling → momentum weakening
Histogram color (green/red) shows the direction and intensity of the move
Background highlights identify potential extremes:
🟩 Green = potential oversold region
🟥 Red = potential overbought region
💡 How to Use
Works on any symbol and timeframe.
Use the Fusion Line’s direction and slope as momentum context, not as direct buy/sell signals.
Combine with price structure, support/resistance, or volume analysis to confirm potential reversals.
Example:
Fusion Line turning upward from green zone → possible bullish momentum shift
Fusion Line turning downward from red zone → possible bearish exhaustion
📘 Notes
Ideal for identifying turning points in ranging or consolidating markets.
Does not generate automated signals or predictions.
Open-source for learning, modification, and educational use.
Designed for clarity, low lag, and clean visualization.
🧩 Developed and shared by TitikSona — made to unify oscillators into one adaptive momentum tool.
RSI VWAP v1 [JopAlgo]RSI VWAP v1.1 made stronger by volume-aware!
We know there's nothing new and the original RSI already does an excellent job. We're just working on small, practical improvements – here's our take: The same basic idea, clearer display, and a single, specially developed rolling line: a VWAP of the RSI that incorporates volume (participation) into the calculation.
Do you prefer the pure classic?
You can still use Wilder or Cutler engines –
but the star here is the VW-RSI + rolling line.
This RSI also offers the possibility of illustrating a possible
POC (Point of Control - or the HAL or VAL) level.
However, the indicator does NOT plot any of these levels itself.
We have included an illustration in the chart for this!
We hope this version makes your decision-making easier.
What you’ll see
The RSI line with a 50 midline and optional bands: either static 70/30 or adaptive μ±k·σ of the Rolling Line.
One smoothing concept only: the Rolling Line (light blue) = VWAP of RSI.
Shadow shading between RSI and the Rolling Line (green when RSI > line, red when RSI < line).
A lighter tint only on the parts of that shadow that sit above the upper band or below the lower band (quick overbought/oversold context).
Simple divergence lines drawn from RSI pivots (green for regular bullish, red for regular bearish). No labels, no buy/sell text—kept deliberately clean.
What’s new, and why it helps
VW-RSI engine (default):
RSI can be computed from volume-weighted up/down moves, so momentum reflects how much traded when price moved—not just the direction.
Rolling Line (VWAP of RSI) with pure VWAP adaptation:
Low volume: blends toward a faster VWAP so early, thin starts aren’t missed.
Volume spikes: blends toward a slower VWAP so a single heavy bar doesn’t whip the curve.
You can reveal the Base Rolling (pre-adaptation) line to see exactly how much adaptation is happening.
Adaptive bands (optional):
Instead of fixed 70/30, use mean ± k·stdev of the Rolling Line over a lookback. Levels breathe with the market—useful in strong trends where static bounds stay pinned.
Minimal, readable panel:
One smoothing, one story. The shadow tells you who’s in control; the lighter highlight shows stretch beyond your lines.
How to read it (fast)
Bias: RSI above 50 (and a rising Rolling Line) → bullish bias; below 50 → bearish bias.
Trigger: RSI crossing the Rolling Line with the bias (e.g., above 50 and crossing up).
Stretch: Near/above the upper band, avoid chasing; near/below the lower band, avoid panic—prefer a cross back through the line.
Divergence lines: Use as context, not as standalone signals. They often help you wait for the next cross or avoid late entries into exhaustion.
Settings that actually matter
RSI Engine: VW-RSI (default), Wilder, or Cutler.
Rolling Line Length: the VWAP length on RSI (higher = calmer, lower = earlier).
Adaptive behavior (pure VWAP):
Speed-up on Low Volume → blends toward fast VWAP (factor of your length).
Dampen Spikes (volume z-score) → blends toward slow VWAP.
Fast/Slow Factors → how far those fast/slow variants sit from the base length.
Bands: choose Static 70/30 or Adaptive μ±k·σ (set the lookback and k).
Visuals: show/hide Base Rolling (ref), main shadow, and highlight beyond bands.
Signal gating: optional “ignore first bars” per day/session if you dislike open noise.
Starter presets
Scalp (1–5m): RSI 9–12, Rolling 12–18, FastFactor ~0.5, SlowFactor ~2.0, Adaptive on.
Intraday (15m–1H): RSI 10–14, Rolling 18–26, Bands k = 1.0–1.4.
Swing (4H–1D): RSI 14–20, Rolling 26–40, Bands k = 1.2–1.8, Adaptive on.
Where it shines (and limits)
Best: liquid markets where volume structure matters (majors, indices, large caps).
Works elsewhere: even with imperfect volume, the shadow + bands remain useful.
Limits: very thin/illiquid assets reduce the benefit of volume-weighting—lengthen settings if needed.
Attribution & License
Based on the concept and baseline implementation of the “Relative Strength Index” by TradingView (Pine v6 built-in).
Released as Open-source (MPL-2.0). Please keep the license header and attribution intact.
Disclaimer
For educational purposes only; not financial advice. Markets carry risk. Test first, use clear levels, and manage risk. This project is independent and not affiliated with or endorsed by TradingView.
TTM Squeeze Screener [Pineify]TTM Squeeze Screener for Multiple Crypto Assets and Timeframes
This advanced TradingView Pine script, TTM Squeeze Screener, helps traders scan multiple crypto symbols and timeframes simultaneously, unlocking new dimensions in momentum and volatility analysis.
Key Features
Screen up to 8 crypto symbols across 4 different timeframes in one pane
TTM Squeeze indicator detects volatility contraction and expansion (“squeeze”) phases
Momentum filter reveals potential breakout direction and strength
Visual screener table for intuitive multi-asset monitoring
Fully customizable for symbols and timeframes
How It Works
The heart of this screener is the TTM Squeeze algorithm—a hybrid volatility and momentum indicator leveraging Bollinger Bands, Keltner Channels, and linear momentum analysis. The script checks whether Bollinger Bands are “squeezed” inside Keltner Channels, flagging periods of low volatility primed for expansion. Once a squeeze is released, the included momentum calculation suggests the likely breakout direction.
For each selected symbol and timeframe, the screener runs the TTM Squeeze logic, outputs “SQUEEZE” or “NO SQZ”, and tags momentum values. A table layout organizes the results, allowing rapid pattern recognition across symbols.
Trading Ideas and Insights
Spot multi-symbol volatility clusters—ideal for finding synchronized market moves
Assess breakout potential and direction before entering trades
Scalping and swing trading decisions are enhanced by cross-timeframe momentum filtering
Portfolio managers can quickly identify which assets are about to move
How Multiple Indicators Work Together
This screener unites three essential concepts:
Bollinger Bands : Measure volatility using standard deviation of price
Keltner Channels : Define expected price range based on average true range (ATR)
Momentum : Linear regression calculation to evaluate the direction and intensity after a squeeze
By combining these, the indicator not only signals when volatility compresses and releases, but also adds directional context—filtering false signals and helping traders time entries and exits more precisely.
Unique Aspects
Multi-symbol, multi-timeframe architecture—optimized for crypto traders and market scanners
Advanced table visualization—see all signals at a glance, minimizing cognitive overload
Modular calculation functions—easy to adapt and extend for other asset classes or strategies
Real-time, low-latency screening—built for actionable alerts on fast-moving markets
How to Use
Add the script to a TradingView chart (works on custom layouts)
Select up to 8 symbols and 4 timeframes using input fields (defaults to BTCUSD, ETHUSD, etc.)
Monitor the screener table; “SQUEEZE” highlights assets in potential breakout phase
Use momentum values to judge if the squeeze is likely bullish or bearish
Combine screener insights with manual chart analysis for optimal results
Customization
Symbols: Easily set any ticker for deep market scanning
Timeframes: Adjust to match your trading horizon (scalping, swing, long-term)
Indicator parameters: Refine Bollinger/Keltner/Momentum settings for sensitivity
Visuals: Personalize table layout, color codes, and formatting for clarity
Conclusion
In summary, the TTM Squeeze Screener is a robust, original TradingView indicator designed for crypto traders who demand a sophisticated multi-symbol, multi-timeframe edge. Its combination of volatility and momentum analytics makes it ideal for catching explosive breakouts, managing risk, and scanning the market efficiently. Whether you’re a scalper or swing trader, this screener provides the insights needed to stay ahead of the curve.
MACD Forecast [Titans_Invest]MACD Forecast — The Future of MACD in Trading
The MACD has always been one of the most powerful tools in technical analysis.
But what if you could see where it’s going, instead of just reacting to what has already happened?
Introducing MACD Forecast — the natural evolution of the MACD Full , now taken to the next level. It’s the world’s first MACD designed not only to analyze the present but also to predict the future behavior of momentum.
By combining the classic MACD structure with projections powered by Linear Regression, this indicator gives traders an anticipatory, predictive view, redefining what’s possible in technical analysis.
Forget lagging indicators.
This is the smartest, most advanced, and most accurate MACD ever created.
🍟 WHY MACD FORECAST IS REVOLUTIONARY
Unlike the traditional MACD, which only reflects current and past price dynamics, the MACD Forecast uses regression-based projection models to anticipate where the MACD line, signal line, and histogram are heading.
This means traders can:
• See MACD crossovers before they happen.
• Spot trend reversals earlier than most.
• Gain an unprecedented timing advantage in both discretionary and automated trading.
In other words: this indicator lets you trade ahead of time.
🔮 FORECAST ENGINE — POWERED BY LINEAR REGRESSION
At its core, the MACD Forecast integrates Linear Regression (ta.linreg) to project the MACD’s future behavior with exceptional accuracy.
Projection Modes:
• Flat Projection: Assumes trend continuity at the current level.
• LinReg Projection: Applies linear regression across N periods to mathematically forecast momentum shifts.
This dual system offers both a conservative and adaptive view of market direction.
📐 ACCURACY WITH FULL CUSTOMIZATION
Just like the MACD Full, this new version comes with 20 customizable buy-entry conditions and 20 sell-entry conditions — now enhanced with forecast-based rules that anticipate crossovers and trend reversals.
You’re not just reacting — you’re strategizing ahead of time.
⯁ HOW TO USE MACD FORECAST❓
The MACD Forecast is built on the same foundation as the classic MACD, but with predictive capabilities.
Step 1 — Spot Predicted Crossovers:
Watch for forecasted bullish or bearish crossovers. These signals anticipate when the MACD line will cross the signal line in the future, letting you prepare trades before the move.
Step 2 — Confirm with Histogram Projection:
Use the projected histogram to validate momentum direction. A rising histogram signals strengthening bullish momentum, while a falling projection points to weakening or bearish conditions.
Step 3 — Combine with Multi-Timeframe Analysis:
Use forecasts across multiple timeframes to confirm signal strength (e.g., a 1h forecast aligned with a 4h forecast).
Step 4 — Set Entry Conditions & Automation:
Customize your buy/sell rules with the 20 forecast-based conditions and enable automation for bots or alerts.
Step 5 — Trade Ahead of the Market:
By preparing for future momentum shifts instead of reacting to the past, you’ll always stay one step ahead of lagging traders.
🤖 BUILT FOR AUTOMATION AND BOTS 🤖
Whether for manual trading, quantitative strategies, or advanced algorithms, the MACD Forecast was designed to integrate seamlessly with automated systems.
With predictive logic at its core, your strategies can finally react to what’s coming, not just what already happened.
🥇 WHY THIS INDICATOR IS UNIQUE 🥇
• World’s first MACD with Linear Regression Forecasting
• Predictive Crossovers (before they appear on the chart)
• Maximum flexibility with Long & Short combinations — 20+ fully configurable conditions for tailor-made strategies
• Fully automatable for quantitative systems and advanced bots
This isn’t just an update.
It’s the final evolution of the MACD.
______________________________________________________
🔹 CONDITIONS TO BUY 📈
______________________________________________________
• Signal Validity: The signal will remain valid for X bars .
• Signal Sequence: Configurable as AND or OR .
🔹 MACD > Signal Smoothing
🔹 MACD < Signal Smoothing
🔹 Histogram > 0
🔹 Histogram < 0
🔹 Histogram Positive
🔹 Histogram Negative
🔹 MACD > 0
🔹 MACD < 0
🔹 Signal > 0
🔹 Signal < 0
🔹 MACD > Histogram
🔹 MACD < Histogram
🔹 Signal > Histogram
🔹 Signal < Histogram
🔹 MACD (Crossover) Signal
🔹 MACD (Crossunder) Signal
🔹 MACD (Crossover) 0
🔹 MACD (Crossunder) 0
🔹 Signal (Crossover) 0
🔹 Signal (Crossunder) 0
🔮 MACD (Crossover) Signal Forecast
🔮 MACD (Crossunder) Signal Forecast
______________________________________________________
______________________________________________________
🔸 CONDITIONS TO SELL 📉
______________________________________________________
• Signal Validity: The signal will remain valid for X bars .
• Signal Sequence: Configurable as AND or OR .
🔸 MACD > Signal Smoothing
🔸 MACD < Signal Smoothing
🔸 Histogram > 0
🔸 Histogram < 0
🔸 Histogram Positive
🔸 Histogram Negative
🔸 MACD > 0
🔸 MACD < 0
🔸 Signal > 0
🔸 Signal < 0
🔸 MACD > Histogram
🔸 MACD < Histogram
🔸 Signal > Histogram
🔸 Signal < Histogram
🔸 MACD (Crossover) Signal
🔸 MACD (Crossunder) Signal
🔸 MACD (Crossover) 0
🔸 MACD (Crossunder) 0
🔸 Signal (Crossover) 0
🔸 Signal (Crossunder) 0
🔮 MACD (Crossover) Signal Forecast
🔮 MACD (Crossunder) Signal Forecast
______________________________________________________
______________________________________________________
🔮 Linear Regression Function 🔮
______________________________________________________
• Our indicator includes MACD forecasts powered by linear regression.
Forecast Types:
• Flat: Assumes prices will stay the same.
• Linreg: Makes a 'Linear Regression' forecast for n periods.
Technical Information:
• Function: ta.linreg()
Parameters:
• source: Source price series.
• length: Number of bars (period).
• offset : Offset.
• return: Linear regression curve.
______________________________________________________
______________________________________________________
⯁ UNIQUE FEATURES
______________________________________________________
Linear Regression: (Forecast)
Signal Validity: The signal will remain valid for X bars
Signal Sequence: Configurable as AND/OR
Table of Conditions: BUY/SELL
Conditions Label: BUY/SELL
Plot Labels in the graph above: BUY/SELL
Automate & Monitor Signals/Alerts: BUY/SELL
Linear Regression (Forecast)
Signal Validity: The signal will remain valid for X bars
Signal Sequence: Configurable as AND/OR
Table of Conditions: BUY/SELL
Conditions Label: BUY/SELL
Plot Labels in the graph above: BUY/SELL
Automate & Monitor Signals/Alerts: BUY/SELL
______________________________________________________
📜 SCRIPT : MACD Forecast
🎴 Art by : @Titans_Invest & @DiFlip
👨💻 Dev by : @Titans_Invest & @DiFlip
🎑 Titans Invest — The Wizards Without Gloves 🧤
✨ Enjoy!
______________________________________________________
o Mission 🗺
• Inspire Traders to manifest Magic in the Market.
o Vision 𐓏
• To elevate collective Energy 𐓷𐓏
🎗️ In memory of João Guilherme — your light will live on forever.
CMC Macro Regime PanelOverview (what it is):
A macro‑regime gate built entirely from TradingView-native symbols (CRYPTOCAP, FRED, DXY/VIX, HYG/LQD). It aggregates central‑bank liquidity (Fed balance sheet − RRP − Treasury General Account), USD strength, credit conditions, stablecoin flows/dominance, tech beta and BTC–NDX co‑move into one normalized score (CLRC). The panel outputs Risk‑ON/OFF regimes, an Early 3/5 pre‑signal, and an automatic BTC vs ETH vs ALTs preference. It is intentionally scoped to Daily & Weekly reads (no intraday timing). Publish with a clean chart and a clear description as per TradingView rules.
TradingView
Why we also use other TradingView screens (and why that is compliant)
This script pulls data via request.security() from official TV symbols only; users often want to open the raw series on separate charts to sanity‑check:
CRYPTOCAP indices: TOTAL, TOTAL2, TOTAL3 (market cap aggregates) and dominance tickers like BTC.D, USDT.D. Helpful for regime & rotation (ALTs vs BTC). TradingView provides definitions for crypto market cap and dominance symbols.
TradingView
+3
TradingView
+3
TradingView
+3
FRED releases: WALCL (Fed assets, weekly), RRPONTSYD (ON RRP, daily), WTREGEN (TGA, weekly), M2SL (M2, monthly). These are the official macro sources exposed on TV.
FRED
+3
FRED
+3
FRED
+3
Risk proxies: TVC:DXY (USD index), TVC:VIX (implied vol), AMEX:HYG/AMEX:LQD (credit), NASDAQ:NDX (tech beta), BINANCE:ETHBTC. VIX/NDX relationship is well-documented; VIX measures 30‑day expected S&P500 vol.
TradingView
+2
TradingView
+2
Compliance note: Using multiple screens is optional for users, but it explains/justifies how components work together (a requirement for public scripts). Keep publication chart clean; use extra screens only to illustrate in the description.
TradingView
How it works (high level)
Liquidity block (Weekly/Monthly)
Net Liquidity = WALCL − RRPONTSYD − WTREGEN (YoY z‑score). WALCL is weekly (as of Wednesday) via H.4.1; RRP is daily; TGA is a Fed liability series. M2 YoY is monthly.
FRED
+3
FRED
+3
FRED
+3
Risk conditions (Daily)
DXY 3‑month momentum (inverted), VIX level (inverted), Credit (HYG/LQD ratio or HY OAS). VIX is a 30‑day constant‑maturity implied vol index per Cboe methodology.
Cboe
+1
Crypto‑internal (Daily)
Stablecoins (USDT+USDC+DAI 30‑day log change), USDT dominance (20‑day, inverted), TOTAL3 (63‑day momentum). Dominance symbols on TV follow a documented formula.
TradingView
Beta & co‑move (Daily)
NDX 63‑day momentum, BTC↔NDX 90‑day correlation.
All components become z‑scores (optionally clipped), weighted, missing inputs drop and weights renormalize. We never use lookahead; we confirm on bar close to avoid repainting per Pine docs (barstate.isconfirmed, multi‑TF).
TradingView
+2
TradingView
+2
What you see on the chart
White line (CLRC) = macro regime score.
Background: Green = Risk‑ON, Red = Risk‑OFF, Teal = Early 3/5 (pre‑signal).
Table: shows each component’s z‑score and the Preference: BTC / ETH / ALTs / Mixed.
Signals & interpretation
Designed for Daily (1D) and Weekly (1W) only.
Regime gates (default Fast preset):
Enter ON: CLRC ≥ +0.8; Hold ON while ≥ +0.5.
Enter OFF: CLRC ≤ −1.0; Hold OFF while ≤ −0.5.
0 / ±1 reading: CLRC is a standardized composite.
~0 = neutral baseline (no macro edge).
≥ +1 = strong macro tailwind (≈ +1σ).
≤ −1 = strong headwind (≈ −1σ).
Early 3/5 (teal): a fast pre‑signal when at least 3 of 5 daily checks align: USDT.D↓, DXY↓, VIX↓, HYG/LQD↑, ETHBTC↑ or TOTAL3↑. It often precedes a full ON flip—use for pre‑positioning rather than full sizing.
BTC/ETH/ALTs selector (only when ON):
ALTs when BTC.D↓ and (ETHBTC↑ or TOTAL3↑) ⇒ rotate down the risk curve.
BTC when BTC.D↑ and ETHBTC↓ ⇒ keep it concentrated.
ETH when ETHBTC↑ while BTC.D flat/up ⇒ add ETH beta.
(Dominance mechanics are documented by TV.)
TradingView
Dissonance (incompatibility) rules — when to stand down
Use these overrides to avoid false comfort:
CLRC > +1 but USDT.D↑ and/or VIX spikes day‑over‑day → downgrade to Neutral; wait for USDT.D to stabilize and VIX to cool (VIX is a fear gauge of 30‑day expectation).
Cboe Global Markets
CLRC > +1 but DXY↑ sharply (USD squeeze) → size below normal; require DXY momentum to roll over.
CLRC < −1 but Early 3/5 = true two days in a row → start reducing underweights; look for ON flip within a few bars.
NetLiq improving (W) but credit (HYG/LQD) deteriorating (D) → treat as mixed regime; prefer BTC over ALTs.
How to use (step‑by‑step)
A. Read on Daily (1D) — main regime
Open CRYPTOCAP:TOTAL3, 1D (panel applied).
Wait for bar close (use alerts on confirmed bar). Pine docs recommend barstate.isconfirmed to avoid repainting on realtime bars.
TradingView
If ON, check Preference (BTC / ETH / ALTs).
Then drop to 4H on your trading pair for micro entries (this indicator itself is not for intraday timing).
B. Confirm weekly macro (1W) — once per week)
Review WALCL/RRP/TGA after the H.4.1 release on Thursdays ~4:30 pm ET. WALCL is “Weekly, as of Wednesday”; M2 is Monthly—so do not expect daily responsiveness from these.
Federal Reserve
+2
FRED
+2
Recommended check times (practical schedule)
Daily regime read: right after your chart’s daily close (confirmed bar). For consistent timing across crypto, many users set chart timezone to UTC and read ~00:05 UTC; you can change chart timezone in TV’s settings.
TradingView
In‑day monitoring: optional spot checks 16:00 & 20:00 UTC (DXY/VIX move during US hours), but act only after the daily bar confirms.
Weekly macro pass: Thu 21:30–22:30 UTC (after H.4.1 4:30 pm ET) or Fri after daily close, to let weekly FRED series propagate.
Federal Reserve
Limitations & data latency (be explicit)
Higher‑TF data & confirmation: FRED weekly/monthly series will not reflect intraday risk in crypto; we aggregate them for regime, not for entry timing.
Repainting 101: Realtime bars move until close. This script does not use lookahead and follows Pine guidance on multi‑TF series; still, always act on confirmed bars.
TradingView
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Public‑library compliance: Title EN‑only; description starts in EN; clean chart; justify component mash‑up; no lookahead; no unrealistic claims.
TradingView
Alerts you can use
“Macro Risk‑ON (entry)” — fires on ON flip (confirmed bar).
“Macro Risk‑OFF (entry)” — fires on OFF flip.
“Early 3/5” — fires when the teal pre‑signal appears (not a regime flip).
“Preference change” — BTC/ETH/ALTs toggles while ON.
Publish note: Alerts are fine; just avoid implying guaranteed accuracy/performance.
TradingView
Background research (why these inputs matter)
Liquidity → Crypto: Fed H.4.1 timing and series definitions (WALCL, RRP, TGA) formalize the “net liquidity” concept used here.
FRED
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Federal Reserve
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FRED
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Stablecoins ↔ Non‑stable crypto: empirical work shows bi‑directional causality between stablecoin market cap and non‑stable crypto cap; stablecoin growth co‑moves with broader crypto activity.
Global liquidity link: world liquidity positively relates to total crypto market cap; lagged effects are observed at monthly horizons.
VIX/Uncertainty effect: fear shocks impair BTC’s “safe haven” behavior; VIX is a meaningful risk‑off read.
RSI MA Cross + Divergence Signal (V2) Core Logic
RSI + Moving Average
The script calculates a standard RSI (default 14).
It then overlays a moving average (SMA/EMA/WMA, default 9).
When RSI crosses above its MA → bullish momentum.
When RSI crosses below its MA → bearish momentum.
Divergence Filter
Signals are only valid if there’s confirmed divergence:
Bullish divergence: Price makes a lower low, RSI makes a higher low.
Bearish divergence: Price makes a higher high, RSI makes a lower high.
Overbought / Oversold Filter
Optional extra:
Bullish signals only valid if RSI ≤ 30 (oversold).
Bearish signals only valid if RSI ≥ 70 (overbought).
This ensures signals happen in “stretched” conditions.
Risk & Trade Management
Entries taken only when all conditions align.
Exits can be managed with ATR stops, partial take-profits, breakeven moves, and trailing stops (we coded these in the strategy version).
Cooldown, session filters, and daily loss guard to keep risk tight.
🔹 Strengths
✅ High selectivity: Combining RSI cross + divergence + OB/OS means signals are rare but higher quality.
✅ Great at catching reversals: Divergence highlights where price may be running out of steam.
✅ Risk management baked in: ATR stops + partial exits smooth out equity curve.
✅ Works across markets: ES, FX, crypto — anywhere RSI divergences are respected.
✅ Flexible: You can loosen/tighten filters depending on aggressiveness.
🔹 Weaknesses
❌ Lag from pivots: Divergence only confirms after a few bars → you enter late sometimes.
❌ Choppy in ranges: In sideways markets, RSI divergences appear often and whipsaw.
❌ Filters reduce signals: With all filters ON (divergence + OB/OS + trend + session), signals can be very rare — may under-trade.
❌ Not standalone: Needs higher-timeframe context (trend, liquidity pools) to avoid counter-trend entries.
🔹 Best Ways to Trade It
Use Higher Timeframe Bias
Run the strategy on 15m/1H, but only trade in direction of higher timeframe trend (e.g., 4H EMA).
Example: If daily is bullish → only take bullish divergences.
Pair With Structure
Look for signals at key zones: HTF support/resistance, VWAP, or FVGs.
Divergence + RSI cross inside an FVG is a strong entry trigger.
Adjust OB/OS for Volatility
For crypto/FX: use 35/65 instead of 30/70 (markets trend harder).
For ES/S&P: 30/70 works fine.
Risk Management Is King
Use partial exits: take profit at 1R, trail rest.
Size by % of equity (we coded this into the strategy).
Avoid News Spikes
Divergences break down around CPI, NFP, Fed announcements — stay flat.
🔹 When It Shines
Trending markets that make extended pushes → clean divergences.
Reversal zones (oversold → bullish bounce, overbought → bearish fade).
Swing trading (15m–4H) — less noise than 1m/5m scalping.
🔹 When to Avoid
Low volatility chop → lots of false divergences.
During high-impact news → RSI swings wildly.
In strong one-way trends without pullbacks — divergence keeps calling tops/bottoms too early.
✅ Summary:
This is a reversal-focused RSI divergence strategy with strict filters. It’s powerful when combined with higher-timeframe bias + structure confluence, but weak if traded blindly in choppy or news-driven conditions. Best to treat it as a precision entry trigger, not a full system — layer it on top of your FVG/ORB framework for maximum edge.
EMA vs TMA Regime FilterEMA vs TMA Regime Filter
This indicator is built as a visual study tool to compare the behavior of the Exponential Moving Average (EMA) and the Triangular Moving Average (TMA).
The EMA applies an exponential weighting to price data, giving stronger importance to the most recent values. This makes it a faster, more responsive line that reflects short-term momentum. The TMA, by contrast, applies a double-smoothing process (or in the “True TMA” option, a split SMA sequence), which produces a much slower curve. The TMA emphasizes balance over reactivity, often used for filtering noise and observing longer-term structure.
When both are plotted on the same chart, their differences become clear. The shaded region between them highlights times when short-term price dynamics diverge from longer-term smoothing. This is where the idea of “regime” comes in — not as a trading signal, but as a descriptive way of seeing whether market action is currently dominated by speed or by stability.
Users can customize:
Line styles, widths, and colors.
Cloud transparency for visual clarity.
Whether to color bars based on relative position (optional, purely visual).
The goal is not to create a system, but to help traders experiment, observe, and learn how different smoothing techniques can emphasize different aspects of price. By switching between the legacy and true TMA, or adjusting lengths, users can study how each approach interprets the same data differently.
Adaptive Valuation [BackQuant]Adaptive Valuation
What this is
A composite, zero-centered oscillator that standardizes several classic indicators and blends them into one “valuation” line. It computes RSI, CCI, Demarker, and the Price Zone Oscillator, converts each to a rolling z-score, then forms a weighted average. Optional smoothing, dynamic overbought and oversold bands, and an on-chart table make the inputs and the final score easy to inspect.
How it works
Components
• RSI with its own lookback.
• CCI with its own lookback.
• DM (Demarker) with its own lookback.
• PZO (Price Zone Oscillator) with its own lookback.
Standardization via z-score
Each component is transformed using a rolling z-score over lookback bars:
z = (value − mean) ÷ stdev , where the mean is an EMA and the stdev is rolling.
This puts all inputs on a comparable scale measured in standard deviations.
Weighted blend
The z-scores are combined with user weights w_rsi, w_cci, w_dm, w_pzo to produce a single valuation series. If desired, it is then smoothed with a selected moving average (SMA, EMA, WMA, HMA, RMA, DEMA, TEMA, LINREG, ALMA, T3). ALMA’s sigma input shapes its curve.
Dynamic thresholds (optional)
Two ways to set overbought and oversold:
• Static : fixed levels at ob_thres and os_thres .
• Dynamic : ±k·σ bands, where σ is the rolling standard deviation of the valuation over dynLen .
Bands can be centered at zero or around the valuation’s rolling mean ( centerZero ).
Visualization and UI
• Zero line at 0 with gradient fill that darkens as the valuation moves away from 0.
• Optional plotting of band lines and background highlights when OB or OS is active.
• Optional candle and background coloring driven by the valuation.
• Summary table showing each component’s current z-score, the final score, and a compact status.
How it can be used
• Bias filter : treat crosses above 0 as bullish bias and below 0 as bearish bias.
• Mean-reversion context : look for exhaustion when the valuation enters the OB or OS region, then watch for exits from those regions or a return toward 0.
• Signal confirmation : use the final score to confirm setups from structure or price action.
• Adaptive banding : with dynamic thresholds, OB and OS adjust to prevailing variability rather than relying on fixed lines.
• Component tuning : change weights to emphasize trend (raise DM, reduce RSI/CCI) or range behavior (raise RSI/CCI, reduce DM). PZO can help in swing environments.
Why z-score blending helps
Indicators often live on different scales. Z-scoring places them on a common, unitless axis, so a one-sigma move in RSI has comparable influence to a one-sigma move in CCI. This reduces scale bias and allows transparent weighting. It also facilitates regime-aware thresholds because the dynamic bands scale with recent dispersion.
Inputs to know
• Component lookbacks : rsilb, ccilb, dmlb, pzolb control each raw signal.
• Standardization window : lookback sets the z-score memory. Longer smooths, shorter reacts.
• Weights : w_rsi, w_cci, w_dm, w_pzo determine each component’s influence.
• Smoothing : maType, smoothP, sig govern optional post-blend smoothing.
• Dynamic bands : dyn_thres, dynLen, thres_k, centerZero configure the adaptive OB/OS logic.
• UI : toggle the plot, table, candle coloring, and threshold lines.
Reading the plot
• Above 0 : composite pressure is positive.
• Below 0 : composite pressure is negative.
• OB region : valuation above the chosen OB line. Risk of mean reversion rises and momentum continuation needs evidence.
• OS region : mirror logic on the downside.
• Band exits : leaving OB or OS can serve as a normalization cue.
Strengths
• Normalizes heterogeneous signals into one interpretable series.
• Adjustable component weights to match instrument behavior.
• Dynamic thresholds adapt to changing volatility and drift.
• Transparent diagnostics from the on-chart table.
• Flexible smoothing choices, including ALMA and T3.
Limitations and cautions
• Z-scores assume a reasonably stationary window. Sharp regime shifts can make recent bands unrepresentative.
• Highly correlated components can overweight the same effect. Consider adjusting weights to avoid double counting.
• More smoothing adds lag. Less smoothing adds noise.
• Dynamic bands recalibrate with dynLen ; if set too short, bands may swing excessively. If too long, bands can be slow to adapt.
Practical tuning tips
• Trending symbols: increase w_dm , use a modest smoother like EMA or T3, and use centerZero dynamic bands.
• Choppy symbols: increase w_rsi and w_cci , consider ALMA with a higher sigma , and widen bands with a larger thres_k .
• Multiday swing charts: lengthen lookback and dynLen to stabilize the scale.
• Lower timeframes: shorten component lookbacks slightly and reduce smoothing to keep signals timely.
Alerts
• Enter and exit of Overbought and Oversold, based on the active band choice.
• Bullish and bearish zero crosses.
Use alerts as prompts to review context rather than as stand-alone trade commands.
Final Remarks
We created this to show people a different way of making indicators & trading.
You can process normal indicators in multiple ways to enhance or change the signal, especially with this you can utilise machine learning to optimise the weights, then trade accordingly.
All of the different components were selected to give some sort of signal, its made out of simple components yet is effective. As long as the user calibrates it to their Trading/ investing style you can find good results. Do not use anything standalone, ensure you are backtesting and creating a proper system.
