MA Thrust Processor | QuantEdgeB⚡MA Thrust Processor | QuantEdgeB
🔭 What is the MA Thrust Processor?
The MA Thrust Processor (MTP) is a dynamic and modular market momentum engine that specializes in thrust-based signal analysis derived from smoothed moving averages. It’s engineered to model directional commitment, detect signal imbalances, and visualize structural momentum in a range of market conditions.
🧬 Think of MTP as a precision-engineered motion sensor — decoding strength, follow-through, and structural imbalance in real time — it detects force, direction, velocity, and alignment, adapting based on your preferred calculation model (Wave, Thrust, Z-Score, or Normalized) and signal mode (Impulse, Trend, or HA Candles).
_______________________
1. 🔧 System Core: Customizability and Processing Engine
📊 Moving Average Types
MA Thrust Processor supports a rich menu of 13+ moving average styles:
• Standard: SMA, EMA, WMA
• Advanced: HMA, LSMA, ALMA, JMA, TEMA, DEMA, SMMA
• Volume-Based: VWMA
• Adaptive Models: VIDYA (3 modes), FRAMA
💡 Each MA type acts as the backbone for signal smoothing and thrust deviation modeling, giving the user tight control over behavior and lag tradeoffs.
⚙️ Calculation Methods (MA Derivatives)
You choose how the core value is calculated:
1️⃣ 𝓦𝓪𝓿𝓮
• Sine-wave modeled oscillator
• Combines MA distance with standard deviation normalization
• Ideal for detecting divergences and cyclical structure
• Output includes center, smoothed line , and histogram.
2️⃣ 𝓣𝓱𝓻𝓾𝓼𝓽
• Calculates MA deviation vs. price and midpoint
• Captures aggressive directional pushes relative to range center
• Excellent for breakout/trend force analysis
3️⃣ 𝓩-𝓢𝓬𝓸𝓻𝓮
• Mean-reverting z-score over MA
• Expresses statistical distance from norm
• Used in reversion or probabilistic strategies
4️⃣ 𝓝𝓸𝓻𝓶𝓪𝓵𝓲𝔃𝓮𝓭
• Scales MA output to 0–1 (centered at 0.5)
• Tracks where the signal lies in its own relative range
• Great for flat vs. trending recognition
_______________________
2. 🧨 SIGNAL MODES – The Behavioral Core
The system supports 3 powerful signal modes that define how the thrust logic is interpreted and visualized.
1️⃣ 𝓘𝓶𝓹𝓾𝓵𝓼𝓮 Mode
🔥 Use Case: Breakouts, aggressive reversals, divergences
🔍 Logic:
• In Wave mode: compares Wave O (oscillator) and S (smoothed)
• In Thrust/Z-Score/Normalized: applies thresholds (static, SD, or percentile)
• Detects sharp departures or rejections from bounds
🎯 Ideal for:
• Scalp or event trades
• High-velocity moves
• Leading edge of trend or compression breaks
2️⃣ 𝓣𝓻𝓮𝓷𝓭 Mode
🧭 Use Case: Stable continuation and trend following
🔍 Logic:
• Signal triggers when value crosses a calculated midline or long-term average
• Thresholds: midline or 365-SMA of smoothed value
• Acts as a stable “bias direction” for trades
🎯 Ideal for:
• Swing trading
• Portfolio allocations
• Holding directional exposure longer
3️⃣ 𝓗𝓐 𝓒𝓪𝓷𝓭𝓵𝓮𝓼 Mode
🎨 Use Case: Visual clarity + phase detection
🔍 Logic:
• Converts signal to Heikin-Ashi candles
• Adds logic for momentum, reversal, continuation, or chop
• Highly discretionary and pattern-oriented
🎯 Ideal for:
• Visual traders
• Phase-based strategies
• Avoiding false positives in chop
_______________________
3. 📊 System Sensor Table (Strength Meter)
MA Thrust Processor includes a sophisticated sensor display with multi-layered insights.
🔁 Signal State
• Long ⟹ bullish conviction or thrust
• Short ⟹ bearish dominance or rejection
• Cash ⟹ neutrality or low conviction
Dynamically generated by logic mode and indicator thresholds.
📊 Strength Bars: Conviction + Potential
Strength output is never binary — instead, it’s expressed via:
1️⃣ Conviction Strength (when signal is active):
• Score from 0% to 100%
• Based on:
o Momentum velocity (Rate of Change)
o Distance from key thresholds
o Divergence signal (Wave mode)
o Flat signal detection (for Normalized)
2️⃣ Potential Strength (when signal = neutral):
• Displays both Bullish and Bearish readiness
• Interprets which side is loading pressure
• Helps traders spot “who has the edge” before breakout
👀 In Wave Mode, potential is calculated from oscillator vs. smoothed
👀 In Impulse/Trend, it blends distance + RoC + signal stability
🔸 HA Candle Phase (HA Mode Only)
When HA mode is active, strength bars are replaced with a live phase classifier:
• Momentum Up/Down: price above/below threshold + same color trend
• Reversal Up/Down: price above/below bounds, opposite signal color
• Continuation Up/Down: following a breakout/confirmation
• Chop: indecision zone
• Neutral: no clear trend
This turns HA mode into a narrative engine, expressing what’s happening, why, and what might come next.
_______________________
4. 🧠 Smart Auto-Configuration
Enabling Use Recommended Settings triggers optimized configurations:
• Pre-set thresholds (static, percentile, SD)
• Ideal lengths for each logic type
• Proper bounds scaling
• MA selections that match the logic
For example:
• Impulse + Thrust ⇒ shorter length + SD
• Trend + Z-Score ⇒ long mean-based
• Wave ⇒ balanced smoothing + SD blend
_______________________
5. 🧪 Summary of Use Cases
Each mode and calculation method within the MA Thrust Processor is tailored for specific trading styles and market conditions. Here’s how to think about their best applications:
🔹 Signal Modes
Impulse Mode is designed for speed and responsiveness. It excels in fast markets where breakouts or sharp reversals happen quickly. Ideal for scalpers, intraday traders, or anyone looking to catch momentum just as it ignites. It’s particularly effective around high-impact events like economic reports or news catalysts, as it picks up directional shifts rapidly.
Trend Mode focuses on longer-term, stable price action. It identifies directional bias using midline or average-based thresholds, making it best for swing traders and trend followers. Because of its stability, it filters out minor fluctuations and helps you stay in trades longer when the directional move is sustained.
HA Candles Mode is for traders who prefer a visual, phase-based approach. It smooths data using Heikin-Ashi logic and adds interpretive layers like "Momentum," "Reversal," or "Chop" to describe what’s happening structurally. This is excellent for discretionary traders, those who use price rhythm or structure, and those seeking cleaner entry points in noisy environments.
🔹 Calculation Methods
Wave is an oscillator-based model. It detects momentum swings and divergence between price and the smoothed oscillator. Great for spotting early reversals, pullback continuations, or cyclical rhythm patterns. In Impulse mode, it shows histogram shifts that reflect internal thrust dynamics.
Thrust quantifies directional pressure by comparing the signal’s distance from both the midpoint of price range and an SMA. This method is powerful when you want to assess how much true force is behind a move — especially useful during breakout scenarios or strong directional pushes.
Z-Score mode normalizes the signal to its statistical distance from the mean. This makes it ideal for mean reversion strategies or situations where price has deviated too far from historical behavior. Traders can look for exhaustion zones or pullback opportunities with greater confidence.
Normalized rescales the signal within a 0–1 range (centered at 0.5), helping traders understand where the price sits within its own context — whether it’s relatively extended or compressed. It’s great for range traders, flat market identification, or mapping gradual bias accumulation.
_______________________
Each mode and method has been thoughtfully designed to align with different strategy frameworks — and switching between them completely reconfigures the way the system operates, giving traders unmatched flexibility across timeframes and asset classes
_______________________
🧭 Conclusion
MA Thrust Processor isn’t just a tool - it’s a precision-calibrated thrust engine that gives market context form. It lets you define your logic, style, and MA behavior while delivering rich visual output and conviction-based strength insight.
Whether you're reading momentum waves, modeling thrust deviation, or interpreting candle structure, MTP adapts to your strategy.
🚀 From short-term scalps to long-term rotations, MTP delivers signal clarity with the quantitative conviction needed in modern markets.
📌 Trade with Statistical Precision | Powered by QuantEdgeB
🔹 Disclaimer: Past performance is not indicative of future results.
🔹 Strategic Advice: Always backtest, optimize, and align parameters with your trading objectives and risk tolerance before live trading.
Cerca negli script per "binary"
Adaptive Pulsar Momentum | QuantEdgeB⚡ Adaptive Pulsar Momentum | QuantEdgeB
🔭 What is Adaptive Pulsar Momentum?
The Adaptive Pulsar Momentum (APM) is a high-performance, modular trading system designed to decode market momentum across a range of conditions. It combines multi-indicator adaptability (RSI, MFI, Z-Score, ROC, and a hybrid AVG mode) with dynamic signal generation using five advanced "modes" of signal logic: Impulse, Trend, Heikin-Ashi Candles, Statistical Deviation, and MACD.
💡 Think of APM as a scientific instrument, scanning, adapting, and broadcasting precision-tuned momentum data in real-time, helping traders eliminate noise, guesswork, and lag.
___________________________________
1.🔧 System Core: Customizability and Adaptation
📊 Indicator Modes
• 𝓡𝓢𝓘 (Relative Strength Index): Classic oscillator detecting overbought/oversold zones.
• 𝓩-𝓢𝓒𝓞𝓡𝓔: Normalized deviation from mean; ideal for statistical reversion plays.
• 𝓜𝓕𝓘 (Money Flow Index): Volume-weighted RSI-style metric.
• 𝓡𝓞𝓒 (Rate of Change): Measures the velocity of price change.
• 𝓐𝓥𝓖: Combines RSI, MFI, Z-Score, and ROC into a unified signal (normalized to 0–100 scale).
🧠 MA Engine (Smoothing)
Over a dozen moving average types:
• Includes ALMA, TEMA, JMA, SMMA, HMA, LSMA, VWMA, and more.
• Dynamic smoothing makes this system versatile across markets and timeframes.
___________________________________
2.🧨 SIGNAL MODES – THE ENGINE ROOM
Each mode turns the raw smoothed indicator into a powerful momentum signal with thresholds and logic specific to the use case.
1️⃣ 𝓘𝓶𝓹𝓾𝓵𝓼𝓮 Mode
🚀 Use case:
Best for detecting explosive, fast-moving momentum before the crowd catches on.
🔍 Logic:
• Thresholds can be Static, Percentile-based, or Standard Deviation derived.
• Dynamic signal: +1 for breakout, -1 for breakdown, 0 for neutral.
• Custom threshold percentiles enable precise tuning.
🎯 Ideal for:
• Scalping breakouts
• Event-driven spikes (e.g., CPI, FOMC)
• Early trend initiation
2️⃣ 𝓣𝓻𝓮𝓷𝓭 Mode
🧭 Use case:
Built to identify and follow trends with minimal noise. Stable, low-churn logic for riding moves.
🔍 Logic:
• Signal generated via cross above/below a calculated midline (either fixed or dynamic mean).
• Best paired with SMMA or TEMA smoothing.
🎯 Ideal for:
• Swing traders
• Momentum trend followers
• Portfolio rotation strategies
3️⃣ 𝓗𝓐 𝓒𝓪𝓷𝓭𝓵𝓮𝓼 Mode
🔥 Use case:
Filters volatility while capturing structural momentum shifts using Heikin-Ashi logic on smoothed indicators.
🔍 Logic:
• Converts the smoothed signal into Heikin-Ashi candles.
• Measures close vs open to determine trend direction.
• Thresholds again can be static, percentile, or SD-based.
🎯 Ideal for:
• Visual trend clarity
• Avoiding whipsaws in sideways markets
• Discretionary trading with cleaner structure
• Mean-Reverting
4️⃣ 𝓢𝓽𝓪𝓽𝓲𝓼𝓽𝓲𝓬𝓪𝓵 𝓓𝓮𝓿𝓲𝓪𝓽𝓲𝓸𝓷 Mode
🧪 Use case:
Detects high-volatility expansions before or during major directional surges.
🔍 Logic:
• Calculates absolute deviation using HA open vs close.
• Filters this with a moving average and overlays a volatility cloud.
• Breaks above/below the cloud signal directional surge.
🎯 Ideal for:
• Pre-breakout scanning
• Identifying regime shifts
• Options traders looking for volatility expansions
5️⃣ 𝓜𝓐𝓒𝓓 Mode
🧲 Use case:
Classic MACD principles adapted to smoothed momentum indicators—ideal for trend continuation or crossovers.
🔍 Logic:
• MACD line = Pulsar signal - EMA of signal.
• Thresholds (up/down) define bias.
• Optional extra filter to validate with midline crossing.
🎯 Ideal for:
• Trend confirmation
• Crossover-based entry strategies
• Confluence with higher timeframe bias
___________________________________
3.📊 System Sensor Table
Adaptive Pulsar Momentum includes a live multi-layered analytics table designed to give traders a complete pulse on current market behavior. Here's what each section reveals:
🔁 System Signal
At any given bar, the algorithm outputs one of three states:
• Long ⟹ Bullish conditions are active and sustained
• Short ⟹ Bearish momentum dominates
• Cash ⟹ Neutral zone — conditions lack a strong directional bias
This is dynamically adjusted based on the selected signal mode (Impulse, Trend, etc.) and adapts in real time to shifts in smoothed oscillator logic or candle structure.
📊 Strength: Conviction & Potential
Unlike binary signals, this table offers graded insights into how strong or fragile the signal actually is, a huge upgrade from traditional systems.
There are two distinct layers:
1. Conviction Strength –> shown when the system is in a full long or short signal.
- A value like “Long Strength: 84%” means there's high confidence in the continuation or follow-through of the current bias.
- It blends distance from threshold, momentum velocity (Rate of Change), and position in range to avoid false positives and overstretched signals.
2. Potential Strength –> shown during neutral (Cash) periods.
- Two bars appear: one for bullish potential, another for bearish potential.
- These answer: “If the market were to move soon, which side has the edge?”
- Example: "↗ 68% / ↘ 32%" means bulls have more pent-up energy or structure.
These bars provide pre-signal tension, helping traders anticipate breakouts or avoid traps during choppy periods.
🔸 HA Candle Phase (When Mode = HA Candles)
Instead of showing strength bars, this mode displays a phase label, interpreting the Heikin-Ashi candle structure in context of momentum and thresholds:
- Momentum Up / Down –> Strong impulse direction confirmed above or below dynamic bounds.
- Reversal Up / Down –> Early signs of potential reversals (price beyond bounds but opposite signal ).
- Continuation Up / Down –> Sustained movement after a signal confirmation (post-threshold cross).
- Chop –> Sideways indecisiveness, often signaling to reduce risk or await clarity.
- Neutral –> No active momentum or pattern signal.
This provides a narrative view of market behavior, ideal for discretionary traders who rely on visual rhythm and pattern recognition.
___________________________________
5. 🧠 Optional Smart Configuration
Enable “Use Recommended Settings” to auto-configure:
• Optimized lengths
• Best-suited moving averages
• Signal type filters
• Volatility lookbacks
Perfect for those wanting precision without manual tuning.
___________________________________
6.🧪 Use Cases by Mode Summary
🔹 Impulse Mode
Ideal for traders looking to capitalize on sharp breakouts or high-momentum reversals. This mode is built for speed and sensitivity, making it a go-to for scalping, reacting to news events, or identifying trends at their earliest inflection points.
🔹 Trend Mode
Engineered for longer-term positioning, this mode tracks sustained directional bias over time. Best suited for swing traders or those managing portfolio allocations, it's focused on the midline dynamics that define trend health and commitment.
🔹 HA Candles Mode
This mode filters out noise through smoothed Heikin-Ashi transformations, providing clean visual structure. It's perfect for discretionary traders, pattern recognizers, or those looking to enter pullbacks within broader trends. The phase system (e.g. Momentum, Reversal, Chop) adds narrative context to price action.
🔹 Statistical Deviation Mode
A quantitative engine for traders who thrive on volatility exploitation. By modeling deviations from mean behavior, it's particularly powerful in options strategies, regime detection, or scanning for expansion conditions. This mode excels when price breaks away from standard norms.
🔹 MACD Mode
The classic concept of momentum meets modern smoothing in this variant. Use this for confirmation, spotting divergences, or executing crossover-based strategies. MACD mode gives clarity in ambiguous zones, favoring structured continuation or reversal bias.
Each mode is uniquely crafted for a different style of trader and market environment, and switching between them transforms the entire engine’s behavior
___________________________________
🧭 Conclusion
Adaptive Pulsar Momentum isn’t just a signal tool, it’s a market intelligence system. Whether you’re scalping volatility, swinging trends, or navigating uncertain chop, APM dynamically adjusts to the rhythm of the market. With precision-tuned signal modes, a smart strength matrix, and plug-and-play configuration, it transforms raw momentum into actionable clarity.
📌 Trade with Statistical Precision | Powered by QuantEdgeB
🔹 Disclaimer: Past performance is not indicative of future results.
🔹 Strategic Advice: Always backtest, optimize, and align parameters with your trading objectives and risk tolerance before live trading.
SynchroTrend Oscillator (STO) [PhenLabs]📊 SynchroTrend Oscillator
Version: PineScript™ v5
📌 Description
The SynchroTrend Oscillator (STO) is a multi-timeframe synchronization tool that combines trend information from three distinct timeframes into a single, easy-to-interpret oscillator ranging from -100 to +100.
This indicator solves the common problem of having to analyze multiple timeframe charts separately by consolidating trend direction and strength across different time horizons. The STO helps traders identify when markets are truly synchronized across timeframes, potentially indicating stronger trend conditions and higher probability trading opportunities.
Using either Moving Average crossovers or RSI analysis as the trend definition metric, the STO provides a comprehensive view of market structure that adapts to various trading strategies and market conditions.
🚀 Points of Innovation
Triple-timeframe synchronization in a single view eliminates chart switching
Dual trend detection methods (MA vs Price or RSI) for flexibility across different markets
Dynamic color intensity that automatically increases with signal strength
Scaled oscillator format (-100 to +100) for intuitive trend strength interpretation
Customizable signal thresholds to match your risk tolerance and trading style
Visual alerts when markets reach full synchronization states
🔧 Core Components
Trend Scoring System: Calculates a binary score (+1, -1, or 0) for each timeframe based on selected metrics, providing clear trend direction
Multi-Timeframe Synchronization: Combines and scales trend scores from all three timeframes into a single oscillator
Dynamic Visualization: Adjusts color transparency based on signal strength, creating an intuitive visual guide
Threshold System: Provides customizable levels for identifying potentially significant trading opportunities
🔥 Key Features
Triple Timeframe Analysis: Synchronizes three user-defined timeframes (default: 60min, 15min, 5min) into one view
Dual Trend Detection Methods: Choose between Moving Average vs Price or RSI-based trend determination
Adjustable Signal Smoothing: Apply EMA, SMA, or no smoothing to the oscillator output for your preferred signal responsiveness
Dynamic Color Intensity: Colors become more vibrant as signal strength increases, helping identify strongest setups
Customizable Thresholds: Set your own buy/sell threshold levels to match your trading strategy
Comprehensive Alerts: Six different alert conditions for crossing thresholds, zero line, and full synchronization states
🎨 Visualization
Oscillator Line: The main line showing the synchronized trend value from -100 to +100
Dynamic Fill: Area between oscillator and zero line changes transparency based on signal strength
Threshold Lines: Optional dotted lines indicating buy/sell thresholds for visual reference
Color Coding: Green for bullish synchronization, red for bearish synchronization
📖 Usage Guidelines
Timeframe Settings
Timeframe 1: Default: 60 (1 hour) - Primary higher timeframe for trend definition
Timeframe 2: Default: 15 (15 minutes) - Intermediate timeframe for trend definition
Timeframe 3: Default: 5 (5 minutes) - Lower timeframe for trend definition
Trend Calculation Settings
Trend Definition Metric: Default: “MA vs Price” - Method used to determine trend on each timeframe
MA Type: Default: EMA - Moving Average type when using MA vs Price method
MA Length: Default: 21 - Moving Average period when using MA vs Price method
RSI Length: Default: 14 - RSI period when using RSI method
RSI Source: Default: close - Price data source for RSI calculation
Oscillator Settings
Smoothing Type: Default: SMA - Applies smoothing to the final oscillator
Smoothing Length: Default: 5 - Period for the smoothing function
Visual & Threshold Settings
Up/Down Colors: Customize colors for bullish and bearish signals
Transparency Range: Control how transparency changes with signal strength
Line Width: Adjust oscillator line thickness
Buy/Sell Thresholds: Set levels for potential entry/exit signals
✅ Best Use Cases
Trend confirmation across multiple timeframes
Finding high-probability entry points when all timeframes align
Early detection of potential trend reversals
Filtering trade signals from other indicators
Market structure analysis
Identifying potential divergences between timeframes
⚠️ Limitations
Like all indicators, can produce false signals during choppy or ranging markets
Works best in trending market conditions
Should not be used in isolation for trading decisions
Past performance is not indicative of future results
May require different settings for different markets or instruments
💡 What Makes This Unique
Combines three timeframes in a single visualization without requiring multiple chart windows
Dynamic transparency feature that automatically emphasizes stronger signals
Flexible trend definition methods suitable for different market conditions
Visual system that makes multi-timeframe analysis intuitive and accessible
🔬 How It Works
1. Trend Evaluation:
For each timeframe, the indicator calculates a trend score (+1, -1, or 0) using either:
MA vs Price: Comparing close price to a moving average
RSI: Determining if RSI is above or below 50
2. Score Aggregation:
The three trend scores are combined and then scaled to a range of -100 to +100
A value of +100 indicates all timeframes show bullish conditions
A value of -100 indicates all timeframes show bearish conditions
Values in between indicate varying degrees of alignment
3. Signal Processing:
The raw oscillator value can be smoothed using EMA, SMA, or left unsmoothed
The final value determines line color, fill color, and transparency settings
Threshold levels are applied to identify potential trading opportunities
💡 Note:
The SynchroTrend Oscillator is most effective when used as part of a comprehensive trading strategy that includes proper risk management techniques. For best results, consider using the oscillator in conjunction with support/resistance levels, price action analysis, and other complementary indicators that align with your trading style.
Ultimate Moving Average Crossover Indicator by SAMQUANT📈 Ultimate Moving Average Crossover Indicator | All-in-One MA Strategy
Unlock the power of multiple moving averages in one versatile indicator designed to give you clear, actionable signals in any market condition.
📌 Key Features:
- Supports **all major moving averages**:
- **SMA, EMA, WMA, HMA, RMA, DEMA, TEMA**, and more.
- Each MA is **fully customizable** with different lengths and types for ultimate flexibility.
- **Binary Long/Short signals** based on crossover logic—perfect for alerts, strategies, or discretionary trading.
- **Dynamic background coloring**:
- **Green** for bullish trends
- **Red** for bearish trends
Quickly gauge market direction at a glance.
---
🚀 Why Use This Indicator?
✅ Combines the strength of all major MA types
✅ Customizable to fit any trading style—scalping, swing, or trend following
✅ Built-in alerts ready for your next trade
✅ Visually intuitive with built-in signal clarity
✅ Excellent tool for **confluence-based** strategies
---
Great trades start with great tools. Clarity, precision, and flexibility—this indicator brings it all to your charts. Trade smarter, not harder.
---
> ⚠️ **Disclaimer:**
This script is intended for **educational and informational purposes only**. It does not constitute financial advice. Past performance is not indicative of future results. Always practice sound risk management and test strategies thoroughly before using real capital.
Beep BoopThe Beep Boop indicator is designed to simplify visual trading decisions by combining the concepts of MACD (Moving Average Convergence Divergence) and a customizable EMA trend filter. It provides clear visual cues to help traders quickly assess market momentum and the current trend direction.
### What Makes Beep Boop Unique?
This indicator uniquely modifies the standard MACD histogram to create a simplified binary visualization—highlighting either bullish or bearish momentum clearly. Rather than displaying traditional MACD bars of varying sizes, it assigns fixed positive or negative values to simplify interpretation:
- A positive histogram (fixed at 0.1) indicates bullish momentum.
- A negative histogram (fixed at 0.09) indicates bearish momentum.
Additionally, Beep Boop integrates a configurable EMA (Exponential Moving Average) to filter signals, allowing traders to identify stronger directional moves by comparing the current price action with the EMA trend line:
- Bullish bars (green) appear only when price action is above the EMA.
- Bearish bars (red) appear only when price action is below the EMA.
- Neutral bars (white) appear when price action is uncertain or mixed in relation to the EMA.
### How to Use Beep Boop?
1. Fast and Slow Lengths: Adjust these to configure the MACD calculation for different timeframes or market volatility.
2. EMA Trend: Change this parameter to fine-tune the sensitivity of the EMA filter based on your preferred trading style (short-term, swing, or long-term).
3. Simple or Exponential MA: Toggle between SMA (Simple Moving Average) or EMA calculations to personalize the responsiveness of the MACD and signal lines.
### Recommended Applications
- Trend-following strategies: Clearly identifies market direction for entries and exits.
- Momentum Trading: Provides simple momentum confirmation for scalping and short-term trading.
- Market Screening: Quickly filters assets based on bullish or bearish momentum strength.
This indicator offers traders a clean, straightforward method to gauge market conditions at a glance, simplifying the complexity inherent in traditional momentum and trend indicators.
Happy Trading!
Fuzzy SMA with DCTI Confirmation[FibonacciFlux]FibonacciFlux: Advanced Fuzzy Logic System with Donchian Trend Confirmation
Institutional-grade trend analysis combining adaptive Fuzzy Logic with Donchian Channel Trend Intensity for superior signal quality
Conceptual Framework & Research Foundation
FibonacciFlux represents a significant advancement in quantitative technical analysis, merging two powerful analytical methodologies: normalized fuzzy logic systems and Donchian Channel Trend Intensity (DCTI). This sophisticated indicator addresses a fundamental challenge in market analysis – the inherent imprecision of trend identification in dynamic, multi-dimensional market environments.
While traditional indicators often produce simplistic binary signals, markets exist in states of continuous, graduated transition. FibonacciFlux embraces this complexity through its implementation of fuzzy set theory, enhanced by DCTI's structural trend confirmation capabilities. The result is an indicator that provides nuanced, probabilistic trend assessment with institutional-grade signal quality.
Core Technological Components
1. Advanced Fuzzy Logic System with Percentile Normalization
At the foundation of FibonacciFlux lies a comprehensive fuzzy logic system that transforms conventional technical metrics into degrees of membership in linguistic variables:
// Fuzzy triangular membership function with robust error handling
fuzzy_triangle(val, left, center, right) =>
if na(val)
0.0
float denominator1 = math.max(1e-10, center - left)
float denominator2 = math.max(1e-10, right - center)
math.max(0.0, math.min(left == center ? val <= center ? 1.0 : 0.0 : (val - left) / denominator1,
center == right ? val >= center ? 1.0 : 0.0 : (right - val) / denominator2))
The system employs percentile-based normalization for SMA deviation – a critical innovation that enables self-calibration across different assets and market regimes:
// Percentile-based normalization for adaptive calibration
raw_diff = price_src - sma_val
diff_abs_percentile = ta.percentile_linear_interpolation(math.abs(raw_diff), normLookback, percRank) + 1e-10
normalized_diff_raw = raw_diff / diff_abs_percentile
normalized_diff = useClamping ? math.max(-clampValue, math.min(clampValue, normalized_diff_raw)) : normalized_diff_raw
This normalization approach represents a significant advancement over fixed-threshold systems, allowing the indicator to automatically adapt to varying volatility environments and maintain consistent signal quality across diverse market conditions.
2. Donchian Channel Trend Intensity (DCTI) Integration
FibonacciFlux significantly enhances fuzzy logic analysis through the integration of Donchian Channel Trend Intensity (DCTI) – a sophisticated measure of trend strength based on the relationship between short-term and long-term price extremes:
// DCTI calculation for structural trend confirmation
f_dcti(src, majorPer, minorPer, sigPer) =>
H = ta.highest(high, majorPer) // Major period high
L = ta.lowest(low, majorPer) // Major period low
h = ta.highest(high, minorPer) // Minor period high
l = ta.lowest(low, minorPer) // Minor period low
float pdiv = not na(L) ? l - L : 0 // Positive divergence (low vs major low)
float ndiv = not na(H) ? H - h : 0 // Negative divergence (major high vs high)
float divisor = pdiv + ndiv
dctiValue = divisor == 0 ? 0 : 100 * ((pdiv - ndiv) / divisor) // Normalized to -100 to +100 range
sigValue = ta.ema(dctiValue, sigPer)
DCTI provides a complementary structural perspective on market trends by quantifying the relationship between short-term and long-term price extremes. This creates a multi-dimensional analysis framework that combines adaptive deviation measurement (fuzzy SMA) with channel-based trend intensity confirmation (DCTI).
Multi-Dimensional Fuzzy Input Variables
FibonacciFlux processes four distinct technical dimensions through its fuzzy system:
Normalized SMA Deviation: Measures price displacement relative to historical volatility context
Rate of Change (ROC): Captures price momentum over configurable timeframes
Relative Strength Index (RSI): Evaluates cyclical overbought/oversold conditions
Donchian Channel Trend Intensity (DCTI): Provides structural trend confirmation through channel analysis
Each dimension is processed through comprehensive fuzzy sets that transform crisp numerical values into linguistic variables:
// Normalized SMA Deviation - Self-calibrating to volatility regimes
ndiff_LP := fuzzy_triangle(normalized_diff, norm_scale * 0.3, norm_scale * 0.7, norm_scale * 1.1)
ndiff_SP := fuzzy_triangle(normalized_diff, norm_scale * 0.05, norm_scale * 0.25, norm_scale * 0.5)
ndiff_NZ := fuzzy_triangle(normalized_diff, -norm_scale * 0.1, 0.0, norm_scale * 0.1)
ndiff_SN := fuzzy_triangle(normalized_diff, -norm_scale * 0.5, -norm_scale * 0.25, -norm_scale * 0.05)
ndiff_LN := fuzzy_triangle(normalized_diff, -norm_scale * 1.1, -norm_scale * 0.7, -norm_scale * 0.3)
// DCTI - Structural trend measurement
dcti_SP := fuzzy_triangle(dcti_val, 60.0, 85.0, 101.0) // Strong Positive Trend (> ~85)
dcti_WP := fuzzy_triangle(dcti_val, 20.0, 45.0, 70.0) // Weak Positive Trend (~30-60)
dcti_Z := fuzzy_triangle(dcti_val, -30.0, 0.0, 30.0) // Near Zero / Trendless (~+/- 20)
dcti_WN := fuzzy_triangle(dcti_val, -70.0, -45.0, -20.0) // Weak Negative Trend (~-30 - -60)
dcti_SN := fuzzy_triangle(dcti_val, -101.0, -85.0, -60.0) // Strong Negative Trend (< ~-85)
Advanced Fuzzy Rule System with DCTI Confirmation
The core intelligence of FibonacciFlux lies in its sophisticated fuzzy rule system – a structured knowledge representation that encodes expert understanding of market dynamics:
// Base Trend Rules with DCTI Confirmation
cond1 = math.min(ndiff_LP, roc_HP, rsi_M)
strength_SB := math.max(strength_SB, cond1 * (dcti_SP > 0.5 ? 1.2 : dcti_Z > 0.1 ? 0.5 : 1.0))
// DCTI Override Rules - Structural trend confirmation with momentum alignment
cond14 = math.min(ndiff_NZ, roc_HP, dcti_SP)
strength_SB := math.max(strength_SB, cond14 * 0.5)
The rule system implements 15 distinct fuzzy rules that evaluate various market conditions including:
Established Trends: Strong deviations with confirming momentum and DCTI alignment
Emerging Trends: Early deviation patterns with initial momentum and DCTI confirmation
Weakening Trends: Divergent signals between deviation, momentum, and DCTI
Reversal Conditions: Counter-trend signals with DCTI confirmation
Neutral Consolidations: Minimal deviation with low momentum and neutral DCTI
A key innovation is the weighted influence of DCTI on rule activation. When strong DCTI readings align with other indicators, rule strength is amplified (up to 1.2x). Conversely, when DCTI contradicts other indicators, rule impact is reduced (as low as 0.5x). This creates a dynamic, self-adjusting system that prioritizes high-conviction signals.
Defuzzification & Signal Generation
The final step transforms fuzzy outputs into a precise trend score through center-of-gravity defuzzification:
// Defuzzification with precise floating-point handling
denominator = strength_SB + strength_WB + strength_N + strength_WBe + strength_SBe
if denominator > 1e-10
fuzzyTrendScore := (strength_SB * STRONG_BULL + strength_WB * WEAK_BULL +
strength_N * NEUTRAL + strength_WBe * WEAK_BEAR +
strength_SBe * STRONG_BEAR) / denominator
The resulting FuzzyTrendScore ranges from -1.0 (Strong Bear) to +1.0 (Strong Bull), with critical threshold zones at ±0.3 (Weak trend) and ±0.7 (Strong trend). The histogram visualization employs intuitive color-coding for immediate trend assessment.
Strategic Applications for Institutional Trading
FibonacciFlux provides substantial advantages for sophisticated trading operations:
Multi-Timeframe Signal Confirmation: Institutional-grade signal validation across multiple technical dimensions
Trend Strength Quantification: Precise measurement of trend conviction with noise filtration
Early Trend Identification: Detection of emerging trends before traditional indicators through fuzzy pattern recognition
Adaptive Market Regime Analysis: Self-calibrating analysis across varying volatility environments
Algorithmic Strategy Integration: Well-defined numerical output suitable for systematic trading frameworks
Risk Management Enhancement: Superior signal fidelity for risk exposure optimization
Customization Parameters
FibonacciFlux offers extensive customization to align with specific trading mandates and market conditions:
Fuzzy SMA Settings: Configure baseline trend identification parameters including SMA, ROC, and RSI lengths
Normalization Settings: Fine-tune the self-calibration mechanism with adjustable lookback period, percentile rank, and optional clamping
DCTI Parameters: Optimize trend structure confirmation with adjustable major/minor periods and signal smoothing
Visualization Controls: Customize display transparency for optimal chart integration
These parameters enable precise calibration for different asset classes, timeframes, and market regimes while maintaining the core analytical framework.
Implementation Notes
For optimal implementation, consider the following guidance:
Higher timeframes (4H+) benefit from increased normalization lookback (800+) for stability
Volatile assets may require adjusted clamping values (2.5-4.0) for optimal signal sensitivity
DCTI parameters should be aligned with chart timeframe (higher timeframes require increased major/minor periods)
The indicator performs exceptionally well as a trend filter for systematic trading strategies
Acknowledgments
FibonacciFlux builds upon the pioneering work of Donovan Wall in Donchian Channel Trend Intensity analysis. The normalization approach draws inspiration from percentile-based statistical techniques in quantitative finance. This indicator is shared for educational and analytical purposes under Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license.
Past performance does not guarantee future results. All trading involves risk. This indicator should be used as one component of a comprehensive analysis framework.
Shout out @DonovanWall
Schaff Trend Cycle (STC)The STC (Schaff Trend Cycle) indicator is a momentum oscillator that combines elements of MACD and stochastic indicators to identify market cycles and potential trend reversals.
Key features of the STC indicator:
Oscillates between 0 and 100, similar to a stochastic oscillator
Values above 75 generally indicate overbought conditions
Values below 25 generally indicate oversold conditions
Signal line crossovers (above 75 or below 25) can suggest potential entry/exit points
Faster and more responsive than traditional MACD
Designed to filter out market noise and identify cyclical trends
Traders typically use the STC indicator to:
Identify potential trend reversals
Confirm existing trends
Generate buy/sell signals when combined with other technical indicators
Filter out false signals in choppy market conditions
This STC implementation includes multiple smoothing options that act as filters:
None: Raw STC values without additional smoothing, which provides the most responsive but potentially noisier signals.
EMA Smoothing: Applies a 3-period Exponential Moving Average to reduce noise while maintaining reasonable responsiveness (default).
Sigmoid Smoothing: Transforms the STC values using a sigmoid (S-curve) function, creating more gradual transitions between signals and potentially reducing whipsaw trades.
Digital (Schmitt Trigger) Smoothing: Creates a binary output (0 or 100) with built-in hysteresis to prevent rapid switching.
The STC indicator uses dynamic color coding to visually represent momentum:
Green: When the STC value is above its 5-period EMA, indicating positive momentum
Red: When the STC value is below its 5-period EMA, indicating negative momentum
The neutral zone (25-75) is highlighted with a light gray fill to clearly distinguish between normal and extreme readings.
Alerts:
Bullish Signal Alert:
The STC has been falling
It bottoms below the 25 level
It begins to rise again
This pattern helps confirm potential uptrend starts with higher reliability.
Bearish Signal Alert:
The STC has been rising
It peaks above the 75 level
It begins to decline
This pattern helps identify potential downtrend starts.
RSI Trend Bias█ OVERVIEW
The RSI Trend Bias indicator is a custom technical analysis tool that utilizes the Relative Strength Index (RSI) to gauge market momentum and identify potential trend shifts. By monitoring RSI crossovers and crossunders relative to customizable threshold levels, the indicator provides clear visual cues that distinguish between bullish and bearish market conditions. This flexible approach makes it suitable for both short-term scalping and longer-term trend analysis.
█ KEY FEATURES
Dynamic RSI Trend Detection
The indicator dynamically determines market bias by monitoring the RSI for crossovers above the upper threshold and crossunders below the lower threshold. This method ensures that only significant momentum shifts trigger a change in trend, reducing false signals in volatile markets.
Adaptive Visualizations
The RSI Trend Bias indicator enhances clarity by plotting the RSI with colors that reflect current market conditions. Additionally, it offers an optional background color change to further emphasize bullish or bearish states, providing immediate visual feedback to traders.
Clear Threshold Indicators
Upper and lower threshold levels are plotted as constant reference lines, clearly delineating overbought and oversold regions. These markers help traders quickly assess market conditions at a glance.
Customizable Settings
Users have full control over key parameters including the RSI length, threshold levels, and visual settings. This customization allows the indicator to be tailored for different markets and trading styles, ensuring optimal performance across various timeframes.
█ UNDERLYING METHODOLOGY & CALCULATIONS
RSI Calculation
The indicator computes the Relative Strength Index over a user-defined period (default is 14), providing a measure of market momentum that reflects price changes over time.
Trend Determination Logic
By detecting when the RSI crosses above the upper threshold, the indicator signals a shift towards bullish momentum. Conversely, a crossunder below the lower threshold indicates bearish conditions. This straightforward binary approach filters out minor fluctuations, ensuring clarity in trend analysis.
Visual Signal Integration
Based on the detected trend, the RSI line is dynamically colored—green for bullish conditions and red for bearish conditions. An optional background color change further reinforces these signals, offering an immediate visual cue of prevailing market sentiment.
█ HOW TO USE THE INDICATOR
1 — Apply the Indicator
• Add the RSI Trend Bias indicator to a separate pane in your trading platform.
2 — Adjust Settings for Your Market
• RSI Length – Define the period for RSI calculation (default is 14).
• Threshold Levels – Set the upper (default 70) and lower (default 30) thresholds to identify overbought and oversold conditions.
• Visual Customization – Choose the bullish (green) and bearish (red) colors, and enable background color changes to enhance visual trend recognition.
3 — Interpret the Signals
• RSI Line – Observe the dynamically colored RSI line; a shift to green signals bullish momentum, while red indicates bearish conditions.
• Threshold Levels – Use the constant upper and lower lines as reference points for overbought and oversold states.
• Signal Timing – A crossover above the upper threshold or a crossunder below the lower threshold suggests potential entry or exit points.
4 — Integrate with Your Trading Strategy
• Combine RSI Trend Bias signals with other technical analysis tools to confirm market direction.
• Utilize the visual cues for fine-tuning your entry and exit decisions, ensuring robust risk management and optimized trade timing.
█ CONCLUSION
The RSI Trend Bias indicator offers a streamlined yet effective approach to monitoring market momentum. By leveraging the established principles of RSI analysis alongside dynamic visual cues, it enables traders to quickly identify bullish and bearish trends. Its customizable features and clear threshold indicators make it a valuable tool for enhancing technical analysis and making informed trading decisions.
Dual Bayesian For Loop [QuantAlgo]Discover the power of probabilistic investing and trading with Dual Bayesian For Loop by QuantAlgo , a cutting-edge technical indicator that brings statistical rigor to trend analysis. By merging advanced Bayesian statistics with adaptive market scanning, this tool transforms complex probability calculations into clear, actionable signals—perfect for both data-driven traders seeking statistical edge and investors who value probability-based confirmation!
🟢 Core Architecture
At its heart, this indicator employs an adaptive dual-timeframe Bayesian framework with flexible scanning capabilities. It utilizes a configurable loop start parameter that lets you fine-tune how recent price action influences probability calculations. By combining adaptive scanning with short-term and long-term Bayesian probabilities, the indicator creates a sophisticated yet clear framework for trend identification that dynamically adjusts to market conditions.
🟢 Technical Foundation
The indicator builds on three innovative components:
Adaptive Loop Scanner: Dynamically evaluates price relationships with adjustable start points for precise control over historical analysis
Bayesian Probability Engine: Transforms market movements into probability scores through statistical modeling
Dual Timeframe Integration: Merges immediate market reactions with broader probability trends through custom smoothing
🟢 Key Features & Signals
The Adaptive Dual Bayesian For Loop transforms complex calculations into clear visual signals:
Binary probability signal displaying definitive trend direction
Dynamic color-coding system for instant trend recognition
Strategic L/S markers at key probability reversals
Customizable bar coloring based on probability trends
Comprehensive alert system for probability-based shifts
🟢 Practical Usage Tips
Here's how you can get the most out of the Dual Bayesian For Loop :
1/ Setup:
Add the indicator to your TradingView chart by clicking on the star icon to add it to your favorites ⭐️
Start with default source for balanced price representation
Use standard length for probability calculations
Begin with Loop Start at 1 for complete price analysis
Start with default Loop Lookback at 70 for reliable sampling size
2/ Signal Interpretation:
Monitor probability transitions across the 50% threshold (0 line)
Watch for convergence of short and long-term probabilities
Use L/S markers for potential trade signals
Monitor bar colors for additional trend confirmation
Configure alerts for significant trend crossovers and reversals, ensuring you can act on market movements promptly, even when you’re not actively monitoring the charts
🟢 Pro Tips
Fine-tune loop parameters for optimal sensitivity:
→ Lower Loop Start (1-5) for more reactive analysis
→ Higher Loop Start (5-10) to filter out noise
Adjust probability calculation period:
→ Shorter lengths (5-10) for aggressive signals
→ Longer lengths (15-30) for trend confirmation
Strategy Enhancement:
→ Compare signals across multiple timeframes
→ Combine with volume for trade validation
→ Use with support/resistance levels for entry timing
→ Integrate other technical tools for even more comprehensive analysis
Options Series - Anchored VWAP Ribbon➤ AVWAP On different chart symbols:
⭐ Overview and Key Features:
Anchored VWAP Calculation:
The script implements the Anchored Volume Weighted Average Price (AVWAP), a tool used by professional traders to identify key price levels weighted by volume, starting from a specific timestamp (anchor point).
Bullish and Bearish Analysis:
It determines the dominance of bullish or bearish momentum based on the relationship between the close price and AVWAP levels across multiple time points.
Dynamic Visualization:
The background of the chart changes color based on overall bullish or bearish sentiment, making it easier to interpret market trends.
Multi-Time Anchors:
By defining multiple anchor points (e.g., 09:15, 09:20), the script calculates a series of AVWAP values for fine-grained intraday analysis.
Customizable Inputs:
Users can select the source price (e.g., hlc3), date, and time for AVWAP calculation.
⭐ How It Works and Functionality:
AVWAP Logic:
Uses the timestamp() function to establish a reference (anchor point).
Calculates the cumulative weighted price (price * volume) and cumulative volume from this anchor point.
The ratio of these sums gives the AVWAP, which updates dynamically with new bars.
Bullish and Bearish Signals:
Binary flags (1 or 0) are set for each time point depending on whether the closing price is above or below the AVWAP for that time.
Aggregates these flags into AVWAP_bull and AVWAP_bear to represent the overall market sentiment.
Decision Logic:
Determines final market conditions (bullish or bearish dominance) based on aggregated scores.
Visual feedback (background and bar colors) is applied accordingly.
⭐ Visualizations and User Experience:
Background Colors:
Green or red background highlights the overall sentiment (bullish or bearish), providing a quick market overview.
Bar Coloring:
Bars are color-coded based on bullish, bearish, or neutral conditions, making it easier to identify trends directly on the chart.
AVWAP Levels:
The calculated AVWAP values are plotted as colored lines for each anchor point, giving precise intraday levels of significance.
Bright colors (fluorescent green/red) are used for additional clarity when the close price is above or below these levels.
🎨 Settings and Customization:
Anchor Point:
Fully customizable anchor points allow users to set specific dates and times (e.g., 09:15 on December 13, 2024) for AVWAP calculations.
Source Price:
Users can choose from hlc3, close, or any other price source to calculate the AVWAP, tailoring the indicator to their strategy.
Visual Appearance:
The transparency, colors, and line styles are adjustable, enabling users to customize the chart to match their trading preferences.
Dynamic Signals:
The script accommodates numerous AVWAP levels, providing flexibility for scalpers and swing traders alike.
⭐ Uniqueness of the Concept:
Precise Intraday Analysis:
Unlike static VWAP, this script allows anchoring to specific times during the day, offering granular insights into market behavior.
Cumulative Sentiment Approach:
Aggregates signals across multiple time intervals, providing a comprehensive view of intraday momentum rather than a single-point reference.
Blending AVWAP with Visual Feedback:
Combines traditional AVWAP calculations with visually impactful features like background shading and bar coloring to enhance decision-making.
Scalability:
Supports adding multiple additional anchor points and customization for broader applicability in different market conditions.
🚀 Conclusion:
The Anchored VWAP Ribbon script is a powerful tool for traders seeking to analyze price behavior relative to volume-weighted levels anchored at specific times. It provides a visually intuitive way to assess intraday market sentiment, combining traditional technical indicators with customizable visualization features. The script’s flexibility makes it suitable for a variety of trading styles, from scalping to swing trading, while its unique cumulative sentiment logic sets it apart from conventional VWAP tools.
Simple Decesion Matrix Classification Algorithm [SS]Hello everyone,
It has been a while since I posted an indicator, so thought I would share this project I did for fun.
This indicator is an attempt to develop a pseudo Random Forest classification decision matrix model for Pinescript.
This is not a full, robust Random Forest model by any stretch of the imagination, but it is a good way to showcase how decision matrices can be applied to trading and within Pinescript.
As to not market this as something it is not, I am simply calling it the "Simple Decision Matrix Classification Algorithm". However, I have stolen most of the aspects of this machine learning algo from concepts of Random Forest modelling.
How it works:
With models like Support Vector Machines (SVM), Random Forest (RF) and Gradient Boosted Machine Learning (GBM), which are commonly used in Machine Learning Classification Tasks (MLCTs), this model operates similarity to the basic concepts shared amongst those modelling types. While it is not very similar to SVM, it is very similar to RF and GBM, in that it uses a "voting" system.
What do I mean by voting system?
How most classification MLAs work is by feeding an input dataset to an algorithm. The algorithm sorts this data, categorizes it, then introduces something called a confusion matrix (essentially sorting the data in no apparently order as to prevent over-fitting and introduce "confusion" to the algorithm to ensure that it is not just following a trend).
From there, the data is called upon based on current data inputs (so say we are using RSI and Z-Score, the current RSI and Z-Score is compared against other RSI's and Z-Scores that the model has saved). The model will process this information and each "tree" or "node" will vote. Then a cumulative overall vote is casted.
How does this MLA work?
This model accepts 2 independent variables. In order to keep things simple, this model was kept as a three node model. This means that there are 3 separate votes that go in to get the result. A vote is casted for each of the two independent variables and then a cumulative vote is casted for the overall verdict (the result of the model's prediction).
The model actually displays this system diagrammatically and it will likely be easier to understand if we look at the diagram to ground the example:
In the diagram, at the very top we have the classification variable that we are trying to predict. In this case, we are trying to predict whether there will be a breakout/breakdown outside of the normal ATR range (this is either yes or no question, hence a classification task).
So the question forms the basis of the input. The model will track at which points the ATR range is exceeded to the upside or downside, as well as the other variables that we wish to use to predict these exceedences. The ATR range forms the basis of all the data flowing into the model.
Then, at the second level, you will see we are using Z-Score and RSI to predict these breaks. The circle will change colour according to "feature importance". Feature importance basically just means that the indicator has a strong impact on the outcome. The stronger the importance, the more green it will be, the weaker, the more red it will be.
We can see both RSI and Z-Score are green and thus we can say they are strong options for predicting a breakout/breakdown.
So then we move down to the actual voting mechanisms. You will see the 2 pink boxes. These are the first lines of voting. What is happening here is the model is identifying the instances that are most similar and whether the classification task we have assigned (remember out ATR exceedance classifier) was either true or false based on RSI and Z-Score.
These are our 2 nodes. They both cast an individual vote. You will see in this case, both cast a vote of 1. The options are either 1 or 0. A vote of 1 means "Yes" or "Breakout likely".
However, this is not the only voting the model does. The model does one final vote based on the 2 votes. This is shown in the purple box. We can see the final vote and result at the end with the orange circle. It is 1 which means a range exceedance is anticipated and the most likely outcome.
The Data Table Component
The model has many moving parts. I have tried to represent the pivotal functions diagrammatically, but some other important aspects and background information must be obtained from the companion data table.
If we bring back our diagram from above:
We can see the data table to the left.
The data table contains 2 sections, one for each independent variable. In this case, our independent variables are RSI and Z-Score.
The data table will provide you with specifics about the independent variables, as well as about the model accuracy and outcome.
If we take a look at the first row, it simply indicates which independent variable it is looking at. If we go down to the next row where it reads "Weighted Impact", we can see a corresponding percent. The "weighted impact" is the amount of representation each independent variable has within the voting scheme. So in this case, we can see its pretty equal, 45% and 55%, This tells us that there is a slight higher representation of z-score than RSI but nothing to worry about.
If there was a major over-respresentation of greater than 30 or 40%, then the model would risk being skewed and voting too heavily in favour of 1 variable over the other.
If we move down from there we will see the next row reads "independent accuracy". The voting of each independent variable's accuracy is considered separately. This is one way we can determine feature importance, by seeing how well one feature augments the accuracy. In this case, we can see that RSI has the greatest importance, with an accuracy of around 87% at predicting breakouts. That makes sense as RSI is a momentum based oscillator.
Then if we move down one more, we will see what each independent feature (node) has voted for. In this case, both RSI and Z-Score voted for 1 (Breakout in our case).
You can weigh these in collaboration, but its always important to look at the final verdict of the model, which if we move down, we can see the "Model prediction" which is "Bullish".
If you are using the ATR breakout, the model cannot distinguish between "Bullish" or "Bearish", must that a "Breakout" is likely, either bearish or bullish. However, for the other classification tasks this model can do, the results are either Bullish or Bearish.
Using the Function:
Okay so now that all that technical stuff is out of the way, let's get into using the function. First of all this function innately provides you with 3 possible classification tasks. These include:
1. Predicting Red or Green Candle
2. Predicting Bullish / Bearish ATR
3. Predicting a Breakout from the ATR range
The possible independent variables include:
1. Stochastics,
2. MFI,
3. RSI,
4. Z-Score,
5. EMAs,
6. SMAs,
7. Volume
The model can only accept 2 independent variables, to operate within the computation time limits for pine execution.
Let's quickly go over what the numbers in the diagram mean:
The numbers being pointed at with the yellow arrows represent the cases the model is sorting and voting on. These are the most identical cases and are serving as the voting foundation for the model.
The numbers being pointed at with the pink candle is the voting results.
Extrapolating the functions (For Pine Developers:
So this is more of a feature application, so feel free to customize it to your liking and add additional inputs. But here are some key important considerations if you wish to apply this within your own code:
1. This is a BINARY classification task. The prediction must either be 0 or 1.
2. The function consists of 3 separate functions, the 2 first functions serve to build the confusion matrix and then the final "random_forest" function serves to perform the computations. You will need all 3 functions for implementation.
3. The model can only accept 2 independent variables.
I believe that is the function. Hopefully this wasn't too confusing, it is very statsy, but its a fun function for me! I use Random Forest excessively in R and always like to try to convert R things to Pinescript.
Hope you enjoy!
Safe trades everyone!
Percentile Momentum IndicatorInput Parameters:
lengthPercentile: Defines the period used to calculate the percentile values (default: 30).
lengthMomentum: Defines the period for calculating the Rate of Change (ROC) momentum (default: 10).
Core Logic:
Rate of Change (ROC): The script calculates the ROC of the closing price over the specified period (lengthMomentum).
Percentile Calculations: The script calculates two key percentiles:
percentile_upper (80th percentile of the high prices)
percentile_lower (20th percentile of the low prices)
Percentile Average: An average of the upper and lower percentiles is calculated (avg_percentile).
Trade Signals:
Buy Signal: Triggered when the ROC is positive, the close is above the percentile_lower, and the close is above the avg_percentile.
Sell Signal: Triggered when the ROC is negative, the close is below the percentile_upper, and the close is below the avg_percentile.
Trade State Management:
The script uses a binary state: 1 for long (buy) and -1 for short (sell).
The trade state is updated based on buy or sell signals.
Bar Coloring:
Bars are colored dynamically based on the trade state:
Green for long (buy signal).
Red for short (sell signal).
The same color is applied to the percentile and average percentile lines for visual consistency.
Nasan Hull-smoothed envelope The Nasan Hull-Smoothed Envelope indicator is a sophisticated overlay designed to track price movement within an adaptive "envelope." It dynamically adjusts to market volatility and trend strength, using a series of smoothing and volatility-correction techniques. Here's a detailed breakdown of its components, from the input settings to the calculated visual elements:
Inputs
look_back_length (500):
Defines the lookback period for calculating intraday volatility (IDV), smoothing it over time. A higher value means the indicator considers a longer historical range for volatility calculations.
sl (50):
Sets the smoothing length for the Hull Moving Average (HMA). The HMA smooths various lines, creating a balance between sensitivity and stability in trend signals.
mp (1.5):
Multiplier for IDV, scaling the volatility impact on the envelope. A higher multiplier widens the envelope to accommodate higher volatility, while a lower one tightens it.
p (0.625):
Weight factor that determines the balance between extremes (highest high and lowest low) and averages (sma of high and sma of low) in the high/low calculations. A higher p gives more weight to extremes, making the envelope more responsive to abrupt market changes.
Volatility Calculation (IDV)
The Intraday Volatility (IDV) metric represents the average volatility per bar as an exponentially smoothed ratio of the high-low range to the close price. This is calculated over the look_back_length period, providing a base volatility value which is then scaled by mp. The IDV enables the envelope to dynamically widen or narrow with market volatility, making it sensitive to current market conditions.
Composite High and Low Bands
The high and low bands define the upper and lower bounds of the envelope.
High Calculation
a_high:
Uses a multi-period approach to capture the highest highs over several intervals (5, 8, 13, 21, and 34 bars). Averaging these highs provides a more stable reference for the high end of the envelope, capturing both immediate and recent peak values.
b_high:
Computes the average of shorter simple moving averages (5, 8, and 13 bars) of the high prices, smoothing out fluctuations in the recent highs. This generates a balanced view of high price trends.
high_c:
Combines a_high and b_high using the weight p. This blend creates a composite high that balances between recent peaks and smoothed averages, making the upper envelope boundary adaptive to short-term price shifts.
Low Calculation
a_low and b_low:
Similar to the high calculation, these capture extreme lows and smooth low values over the same intervals. This approach creates a stable and adaptive lower bound for the envelope.
low_c:
Combines a_low and b_low using the weight p, resulting in a composite low that adjusts to price fluctuations while maintaining a stable trend line.
Volatility-Adjusted Bands
The final composite high (c_high) and composite low (c_low) bands are adjusted using IDV, which accounts for intraday volatility. When volatility is high, the bands expand; when it’s low, they contract, providing a visual representation of volatility-adjusted price bounds.
Basis Line
The basis line is a Hull Moving Average (HMA) of the average of c_high and c_low. The HMA is known for its smoothness and responsiveness, making the basis line a central trend indicator. The color of the basis line changes:
Green when the basis line is increasing.
Red when the basis line is decreasing.
This color-coded basis line serves as a quick visual reference for trend direction.
Short-Term Trend Strength Block
This component analyzes recent price action to assess short-term bullish and bearish momentum.
Conditions (green, red, green1, red1):
These are binary conditions that categorize price movements as bullish or bearish based on the close compared to the open and the close’s relationship with the exponential moving average (EMA). This separation helps capture different types of strength (above/below EMA) and different bullish or bearish patterns.
Composite Trend Strength Values:
Each of the bullish and bearish counts (above and below the EMA) is normalized, resulting in the following values:
green_EMAup_a and red_EMAup_a for bullish and bearish strength above the EMA.
green_EMAdown_a and red_EMAdown_a for bullish and bearish strength below the EMA.
Trend Strength (t_s):
This calculated metric combines the normalized trend strengths with extra weight to conditions above the EMA, giving more relevance to trends that have momentum behind them.
Enhanced Trend Strength
avg_movement:
Calculates the average absolute price movement over the short_term_length, providing a measurement of recent price activity that scales with volatility.
enhanced_t_s:
Multiplies t_s by avg_movement, creating an enhanced trend strength value that reflects both directional strength and the magnitude of recent price movement.
min and max:
Minimum and maximum percentile thresholds, respectively, based on enhanced_t_s for controlling the color gradient in the fill area.
Fill Area
The fill area between plot_c_high and plot_c_low is color-coded based on the enhanced trend strength (enhanced_t_s):
Gradient color transitions from blue to green based on the strength level, with blue representing weaker trends and green indicating stronger trends.
This visual fill provides an at-a-glance assessment of trend strength across the envelope, with color shifts highlighting momentum shifts.
Summary
The indicator’s purpose is to offer an adaptive price envelope that reflects real-time market volatility and trend strength. Here’s what each component contributes:
Basis Line: A trend-following line in the center that adjusts color based on trend direction.
Envelope (c_high, c_low): Adapts to volatility by expanding and contracting based on IDV, giving traders a responsive view of expected price bounds.
Fill Area: A color-gradient region representing trend strength within the envelope, helping traders easily identify momentum changes.
Overall, this tool helps to identify trend direction, market volatility, and strength of price movements, allowing for more informed decisions based on visual cues around price boundaries and trend momentum.
Probability GoldThis is a leading indicator designed for the 1 minute chart, and while it works on any time frame, I haven't tested it's practicality outside of the 1 minute chart. This uses historical data and applies statistical analysis to key metrics of momentum. The data is filtered by "time of day" as well as "day of week", but be mindful that the "day of week" option reduces your total amount of data points, and will not work well on new stocks. This indicator also uses binary/gaussian distribution concepts to create channels and pockets which act as seemingly magnetic forces. This is all speculative. Do not use this indicator on its own. This is intended to do nothing more than to show you whether if, on average, the historical data under the same time and rate of change conditions, goes for or against your trade. Use it in conjunction with your most trusted and classic indicators. This can act as a small nudge, letting you now the chances of what you have already established in your mind. This is HIGHLY experimental, made by an amateur, and also very pretty. So, enjoy!
M & W Checklistindicator to Validate & Grade M & W Patterns.
Indicator Inputs
Table Color Palette
• Position Valid : Positions the Valid Trade table on the chart.
• Position Grade : Positions the Grade table on the chart, hover over the Column 1 Row 1 for a description of the bands.
• Size: Text size for all tables.
• Text Color : Sets text color.
• Border Color : Sets the table border color for all tables.
• Background Color : Sets table backgroud color for all tables.
Valid Trade Table
Checkboxes to indicate if the trade is valid. Fail is displayed if unchecked, Pass if checked.
Grade Table
• S/R Level 1: distance between neckline and 1st resistance area in % of the total distance between neckline and take profit. This is not for road blocks but pivot points etc before the initial run up/down in price. I have this set to 30% , this means that if there is a pivot point between the neckline and 30% of the TP level I weight it negatively.
• S/R Level 2: distance between neckline and 1st resistance area in % of the total distance between neckline and take profit. This is not for road blocks but pivot points etc before the initial run up/down in price. I have this set to 50% , this means that if there is a pivot point between the neckline and 50% of the TP level 2 weight it negatively but less so than level 1.
• S/R Level 3: distance between neckline and 1st resistance area in % of the total distance between neckline and take profit. This is not for road blocks but pivot points etc before the initial run up/down in price. I have this set to 70% , this means that if there is a pivot point between the neckline and 70% of the TP level 3 weight it negatively but less so than level 1 & level 2.
• Checkboxes are self explanatory, they are binary options, all are weighted negatively if checked and are weighted positively if unchecked. Divergence values for weighting are neutral if unckecked & weighted positively if checked.
• The select options are neutral weighting if set to neutral , if set to For its weighted positive and set to Against weighted negatively.
Technical Specification of the Scoring and Band System
Overview
The scoring system is designed to evaluate a set of technical trade conditions, assigning weights to various criteria that influence the quality of the trade. The system calculates a total score based on both positive and negative conditions. Based on the final score, the system assigns a grade or band (A, B, or C) for positive scores, and a "Negative" label for negative scores.
Scoring System
The system calculates the score by evaluating a set of 12 conditions (gradeCondition1 to gradeCondition12). These conditions are manually input by the user via checkboxes or dropdowns in a technical indicator (written in Pine Script for TradingView). The score weights vary according to the relative importance of each condition.
Condition Breakdown and Weighting:
1. Divergences (GradeCondition1 & GradeCondition2):
◦ 1H Divergence: +5 points if condition is true.
◦ 4H Divergence: +10 points if condition is true (stronger weight than 1H).
2. Support/Resistance at Neckline (GradeCondition3):
◦ Negative if present: -15 points if true (carries significant negative weight).
3. RB near Entry (GradeCondition4):
◦ Very Negative: -20 points if true (this is a critical negative condition).
4. RB can Manage (GradeCondition5):
◦ Slightly Negative: -5 points if true.
5. Institutional Value Zones (GradeCondition6 to GradeCondition8):
◦ For the trade: +5 points.
◦ Against the trade: -5 points.
◦ Neutral: 0 points.
6. S/R between Neckline & Targets (GradeCondition9 to GradeCondition11):
◦ Level 1: -10 points if true, +7 points if false.
◦ Level 2: -7 points if true, +7 points if false.
◦ Level 3: -5 points if true, +7 points if false.
◦ Use fib tool or Gann Box to measure any S/R levels setup according to your preferences.
7. News Timing (GradeCondition12):
◦ News within 3 hours: -20 points if true (strong negative factor).
◦ No upcoming news: +10 points if false.
Scoring Calculation Formula:
totalScore = score1 + score2 + score3 + score4 + score5 + score6 + score7 + score8 + score9 + score10 + score11 + score12
Where:
• score1 to score12 represent the points derived from the conditions described above.
Coloring and Visual Feedback:
• Positive Scores: Displayed in green.
• Negative Scores: Displayed in red.
Band System
The Band System classifies the total score into different grades, depending on the final value of totalScore. This classification provides an intuitive ranking for trades, helping users quickly assess trade quality.
Band Classification:
• Band A: If the totalScore is 41 or more.
◦ Represents a highly favorable trade setup.
• Band B: If the totalScore is between 21 and 40.
◦ Represents a favorable trade setup with good potential.
• Band C: If the totalScore is between 1 and 20.
◦ Represents a trade setup that is acceptable but may have risks.
• Negative: If the totalScore is 0 or less.
◦ Represents a poor trade setup with significant risks or unfavorable conditions.
Band Calculation Logic (in Pine Script):
var string grade = ""
if (totalScore >= 41)
grade := "Band A"
else if (totalScore >= 21)
grade := "Band B"
else if (totalScore >= 1)
grade := "Band C"
else
grade := "Negative"
Technical Key Points:
• Highly Negative Conditions:
◦ The system penalizes certain conditions more heavily, especially those that suggest significant risks (e.g., News in less than 3 hours, RB near Entry).
• Positive Trade Conditions:
◦ Divergences, Institutional Value Zones in favor of the trade, and lack of significant nearby resistance all contribute positively to the score.
• Flexible System:
◦ The system can be adapted or fine-tuned by adjusting the weights of individual conditions according to trading preferences.
Use Case Example:
• If a trade has 1H and 4H Divergence, RB near Entry (negative), and no upcoming news:
◦ 1H Divergence: +5 points.
◦ 4H Divergence: +10 points.
◦ RB near Entry: -20 points.
◦ No news: +10 points.
◦ Total Score: 5 + 10 - 20 + 10 = 5 → Band C.
This modular and flexible scoring system allows traders to systematically evaluate trades and quickly gauge the trade's potential based on technical indicators
Summary:
Maximum Score: 61
Minimum Score: -97
These are the bounds of the score range based on the current logic of the script.
RSI (Kernel Optimized) | Flux Charts💎 GENERAL OVERVIEW
Introducing our new KDE Optimized RSI Indicator! This indicator adds a new aspect to the well-known RSI indicator, with the help of the KDE (Kernel Density Estimation) algorithm, estimates the probability of a candlestick will be a pivot or not. For more information about the process, please check the "HOW DOES IT WORK ?" section.
Features of the new KDE Optimized RSI Indicator :
A New Approach To Pivot Detection
Customizable KDE Algorithm
Realtime RSI & KDE Dashboard
Alerts For Possible Pivots
Customizable Visuals
❓ HOW TO INTERPRET THE KDE %
The KDE % is a critical metric that reflects how closely the current RSI aligns with the KDE (Kernel Density Estimation) array. In simple terms, it represents the likelihood that the current candlestick is forming a pivot point based on historical data patterns. a low percentage suggests a lower probability of the current candlestick being a pivot point. In these cases, price action is less likely to reverse, and existing trends may continue. At moderate levels, the possibility of a pivot increases, indicating potential trend shifts or consolidations.Traders should start monitoring closely for confirmation signals. An even higher KDE % suggests a strong likelihood that the current candlestick could form a pivot point, which could lead to a reversal or significant price movement. These points often align with overbought or oversold conditions in traditional RSI analysis, making them key moments for potential trade entry or exit.
📌 HOW DOES IT WORK ?
The RSI (Relative Strength Index) is a widely used oscillator among traders. It outputs a value between 0 - 100 and gives a glimpse about the current momentum of the price action. This indicator then calculates the RSI for each candlesticks, and saves them into an array if the candlestick is a pivot. The low & high pivot RSIs' are inserted into two different arrays. Then the a KDE array is calculated for both of the low & high pivot RSI arrays. Explaining the KDE might be too much for this write-up, but for a brief explanation, here are the steps :
1. Define the necessary options for the KDE function. These are : Bandwidth & Nº Steps, Array Range (Array Max - Array Min)
2. After that, create a density range array. The array has (steps * 2 - 1) elements and they are calculated by (arrMin + i * stepCount), i being the index.
3. Then, define a kernel function. This indicator has 3 different kernel distribution modes : Uniform, Gaussian and Sigmoid
4. Then, define a temporary value for the current element of KDE array.
5. For each element E in the pivot RSI array, add "kernel(densityRange.get(i) - E, 1.0 / bandwidth)" to the temporary value.
6. Add 1.0 / arrSize * to the KDE array.
Then the prefix sum array of the KDE array is calculated. For each candlestick, the index closest to it's RSI value in the KDE array is found using binary search. Then for the low pivot KDE calculation, the sum of KDE values from found index to max index is calculated. For the high pivot KDE, the sum of 0 to found index is used. Then if high or low KDE value is greater than the activation threshold determined in the settings, a bearish or bullish arrow is plotted after bar confirmation respectively. The arrows are drawn as long as the KDE value of current candlestick is greater than the threshold. When the KDE value is out of the threshold, a less transparent arrow is drawn, indicating a possible pivot point.
🚩 UNIQUENESS
This indicator combines RSI & KDE Algorithm to get a foresight of possible pivot points. Pivot points are important entry, confirmation and exit points for traders. But to their nature, they can be only detected after more candlesticks are rendered after them. The purpose of this indicator is to alert the traders of possible pivot points using KDE algorithm right away when they are confirmed. The indicator also has a dashboard for realtime view of the current RSI & Bullish or Bearish KDE value. You can fully customize the KDE algorithm and set up alerts for pivot detection.
⚙️ SETTINGS
1. RSI Settings
RSI Length -> The amount of bars taken into account for RSI calculation.
Source -> The source value for RSI calculation.
2. Pivots
Pivot Lengths -> Pivot lengths for both high & low pivots. For example, if this value is set to 21; 21 bars before AND 21 bars after a candlestick must be higher for a candlestick to be a low pivot.
3. KDE
Activation Threshold -> This setting determines the amount of arrows shown. Higher options will result in more arrows being rendered.
Kernel -> The kernel function as explained in the upper section.
Bandwidth -> The bandwidth variable as explained in the upper section. The smoothness of the KDE function is tied to this setting.
Nº Bins -> The Nº Steps variable as explained in the upper section. It determines the precision of the KDE algorithm.
Uptrick: Trend SMA Oscillator### In-Depth Analysis of the "Uptrick: Trend SMA Oscillator" Indicator
---
#### Introduction to the Indicator
The "Uptrick: Trend SMA Oscillator" is an advanced yet user-friendly technical analysis tool designed to help traders across all levels of experience identify and follow market trends with precision. This indicator builds upon the fundamental principles of the Simple Moving Average (SMA), a cornerstone of technical analysis, to deliver a clear, visually intuitive overlay on the price chart. Through its strategic use of color-coding and customizable parameters, the Uptrick: Trend SMA Oscillator provides traders with actionable insights into market dynamics, enhancing their ability to make informed trading decisions.
#### Core Concepts and Methodology
1. **Foundational Principle – Simple Moving Average (SMA):**
- The Simple Moving Average (SMA) is the heart of the Uptrick: Trend SMA Oscillator. The SMA is a widely-used technical indicator that calculates the average price of an asset over a specified number of periods. By smoothing out price data, the SMA helps to reduce the noise from short-term fluctuations, providing a clearer picture of the overall trend.
- In the Uptrick: Trend SMA Oscillator, two SMAs are employed:
- **Primary SMA (oscValue):** This is applied to the closing price of the asset over a user-defined period (default is 14 periods). This SMA tracks the price closely and is sensitive to changes in market direction.
- **Smoothing SMA (oscV):** This second SMA is applied to the primary SMA, further smoothing the data and helping to filter out minor price movements that might otherwise be mistaken for trend reversals. The default period for this smoothing is 50, but it can be adjusted to suit the trader's preference.
2. **Color-Coding for Trend Visualization:**
- One of the most distinctive features of this indicator is its use of color to represent market trends. The indicator’s line changes color based on the relationship between the primary SMA and the smoothing SMA:
- **Bullish (Green):** The line turns green when the primary SMA is equal to or greater than the smoothing SMA, indicating that the market is in an upward trend.
- **Bearish (Red):** Conversely, the line turns red when the primary SMA falls below the smoothing SMA, signaling a downward trend.
- This color-coded system provides traders with an immediate, easy-to-interpret visual cue about the market’s direction, allowing for quick decision-making.
#### Detailed Explanation of Inputs
1. **Bullish Color (Default: Green #00ff00):**
- This input allows traders to customize the color that represents bullish trends on the chart. The default setting is green, a color commonly associated with upward market movement. However, traders can adjust this to any color that suits their visual preferences or matches their overall chart theme.
2. **Bearish Color (Default: Red RGB: 245, 0, 0):**
- The bearish color input determines the color of the line when the market is trending downwards. The default setting is a vivid red, signaling caution or selling opportunities. Like the bullish color, this can be customized to fit the trader’s needs.
3. **Line Thickness (Default: 5):**
- This setting controls the thickness of the line plotted by the indicator. The default thickness of 5 makes the line prominent on the chart, ensuring that the trend is easily visible even in complex or crowded chart setups. Traders can adjust the thickness to make the line thinner or thicker, depending on their visual preferences.
4. **Primary SMA Period (Value 1 - Default: 14):**
- The primary SMA period defines how many periods (e.g., days, hours) are used to calculate the moving average based on the asset’s closing prices. The default period of 14 is a balanced setting that offers a good mix of responsiveness and stability, but traders can adjust this depending on their trading style:
- **Shorter Periods (e.g., 5-10):** These make the indicator more sensitive, capturing trends more quickly but also increasing the likelihood of reacting to short-term price fluctuations or "noise."
- **Longer Periods (e.g., 20-50):** These smooth the data more, providing a more stable trend line that is less prone to whipsaws but may be slower to respond to trend changes.
5. **Smoothing SMA Period (Value 2 - Default: 50):**
- The smoothing SMA period determines how much the primary SMA is smoothed. A longer smoothing period results in a more gradual, stable line that focuses on the broader trend. The default of 50 is designed to smooth out most of the short-term fluctuations while still being responsive enough to detect significant trend shifts.
- **Customization:**
- **Shorter Smoothing Periods (e.g., 20-30):** Make the indicator more responsive, better for fast-moving markets or for traders who want to capture quick trends.
- **Longer Smoothing Periods (e.g., 70-100):** Enhance stability, ideal for long-term traders looking to avoid reacting to minor price movements.
#### Unique Characteristics and Advantages
1. **Simplicity and Clarity:**
- The Uptrick: Trend SMA Oscillator’s design prioritizes simplicity without sacrificing effectiveness. By relying on the widely understood SMA, it avoids the complexity of more esoteric indicators while still providing reliable trend signals. This simplicity makes it accessible to traders of all levels, from novices who are just learning about technical analysis to experienced traders looking for a straightforward, dependable tool.
2. **Visual Feedback Mechanism:**
- The indicator’s use of color to signify market trends is a particularly powerful feature. This visual feedback mechanism allows traders to assess market conditions at a glance. The clarity of the green and red color scheme reduces the mental effort required to interpret the indicator, freeing the trader to focus on strategy execution.
3. **Adaptability Across Markets and Timeframes:**
- One of the strengths of the Uptrick: Trend SMA Oscillator is its versatility. The basic principles of moving averages apply equally well across different asset classes and timeframes. Whether trading stocks, forex, commodities, or cryptocurrencies, traders can use this indicator to gain insights into market trends.
- **Intraday Trading:** For day traders who operate on short timeframes (e.g., 1-minute, 5-minute charts), the oscillator can be adjusted to be more responsive, capturing quick shifts in momentum.
- **Swing Trading:** Swing traders, who typically hold positions for several days to weeks, will find the default settings or slightly adjusted periods ideal for identifying and riding medium-term trends.
- **Long-Term Trading:** Position traders and investors can adjust the indicator to focus on long-term trends by increasing the periods for both the primary and smoothing SMAs, filtering out minor fluctuations and highlighting sustained market movements.
4. **Minimal Lag:**
- One of the challenges with moving averages is lag—the delay between when the price changes and when the indicator reflects this change. The Uptrick: Trend SMA Oscillator addresses this by allowing traders to adjust the periods to find a balance between responsiveness and stability. While all SMAs inherently have some lag, the customizable nature of this indicator helps traders mitigate this effect to align with their specific trading goals.
5. **Customizable and Intuitive:**
- While many technical indicators come with a fixed set of parameters, the Uptrick: Trend SMA Oscillator is fully customizable, allowing traders to tailor it to their trading style, market conditions, and personal preferences. This makes it a highly flexible tool that can be adjusted as markets evolve or as a trader’s strategy changes over time.
#### Practical Applications for Different Trader Profiles
1. **Day Traders:**
- **Use Case:** Day traders can customize the SMA periods to create a faster, more responsive indicator. This allows them to capture short-term trends and make quick decisions. For example, reducing the primary SMA to 5 and the smoothing SMA to 20 can help day traders react promptly to intraday price movements.
- **Strategy Integration:** Day traders might use the Uptrick: Trend SMA Oscillator in conjunction with volume-based indicators to confirm the strength of a trend before entering or exiting trades.
2. **Swing Traders:**
- **Use Case:** Swing traders can use the default settings or slightly adjust them to smooth out minor price fluctuations while still capturing medium-term trends. This approach helps in identifying the optimal points to enter or exit trades based on the broader market direction.
- **Strategy Integration:** Swing traders can combine this indicator with oscillators like the Relative Strength Index (RSI) to confirm overbought or oversold conditions, thereby refining their entry and exit strategies.
3. **Position Traders:**
- **Use Case:** Position traders, who hold trades for extended periods, can extend the SMA periods to focus on long-term trends. By doing so, they minimize the impact of short-term market noise and focus on the underlying trend.
- **Strategy Integration:** Position traders might use the Uptrick: Trend SMA Oscillator in combination with fundamental analysis. The indicator can help confirm the timing of entries and exits based on broader economic or corporate developments.
4. **Algorithmic and Quantitative Traders:**
- **Use Case:** The simplicity and clear logic of the Uptrick: Trend SMA Oscillator make it an excellent candidate for algorithmic trading strategies. Its binary output—bullish or bearish—can be easily coded into automated trading systems.
- **Strategy Integration:** Quant traders might use the indicator as part of a larger trading system that incorporates multiple indicators and rules, optimizing the SMA periods based on historical backtesting to achieve the best results.
5. **Novice Traders:**
- **Use Case:** Beginners can use the Uptrick: Trend SMA Oscillator to learn the basics of trend-following strategies.
The visual simplicity of the color-coded line helps novice traders quickly understand market direction without the need to interpret complex data.
- **Educational Value:** The indicator serves as an excellent starting point for those new to technical analysis, providing a practical example of how moving averages work in a real-world trading environment.
#### Combining the Indicator with Other Tools
1. **Relative Strength Index (RSI):**
- The RSI is a momentum oscillator that measures the speed and change of price movements. When combined with the Uptrick: Trend SMA Oscillator, traders can look for instances where the RSI shows divergence from the price while the oscillator confirms the trend. This can be a powerful signal of an impending reversal or continuation.
2. **Moving Average Convergence Divergence (MACD):**
- The MACD is another popular trend-following momentum indicator. By using it alongside the Uptrick: Trend SMA Oscillator, traders can confirm the strength of a trend and identify potential entry and exit points with greater confidence. For example, a bullish crossover on the MACD that coincides with the Uptrick: Trend SMA Oscillator turning green can be a strong buy signal.
3. **Volume Indicators:**
- Volume is often considered the fuel behind price movements. Using volume indicators like the On-Balance Volume (OBV) or Volume Weighted Average Price (VWAP) in conjunction with the Uptrick: Trend SMA Oscillator can help traders confirm the validity of a trend. A trend identified by the oscillator that is supported by increasing volume is typically more reliable.
4. **Fibonacci Retracement:**
- Fibonacci retracement levels are used to identify potential reversal levels in a trending market. When the Uptrick: Trend SMA Oscillator indicates a trend, traders can use Fibonacci retracement levels to find potential entry points that align with the broader trend direction.
#### Implementation in Different Market Conditions
1. **Trending Markets:**
- The Uptrick: Trend SMA Oscillator excels in trending markets, where it provides clear signals on the direction of the trend. In a strong uptrend, the line will remain green, helping traders stay in the trade for longer periods. In a downtrend, the red line will signal the continuation of bearish conditions, prompting traders to stay short or avoid long positions.
2. **Sideways or Range-Bound Markets:**
- In range-bound markets, where price oscillates within a confined range without a clear trend, the Uptrick: Trend SMA Oscillator may produce more frequent changes in color. While this could indicate potential reversals at the range boundaries, traders should be cautious of false signals. It may be beneficial to pair the oscillator with a volatility indicator to better navigate such conditions.
3. **Volatile Markets:**
- In highly volatile markets, where prices can swing rapidly, the sensitivity of the Uptrick: Trend SMA Oscillator can be adjusted by modifying the SMA periods. A shorter SMA period might capture quick trends, but traders should be aware of the increased risk of whipsaws. Combining the oscillator with a volatility filter or using it in a higher time frame might help mitigate some of this risk.
#### Final Thoughts
The "Uptrick: Trend SMA Oscillator" is a versatile, easy-to-use indicator that stands out for its simplicity, visual clarity, and adaptability. It provides traders with a straightforward method to identify and follow market trends, using the well-established concept of moving averages. The indicator’s customizable nature makes it suitable for a wide range of trading styles, from day trading to long-term investing, and across various asset classes.
By offering immediate visual feedback through color-coded signals, the Uptrick: Trend SMA Oscillator simplifies the decision-making process, allowing traders to focus on execution rather than interpretation. Whether used on its own or as part of a broader technical analysis toolkit, this indicator has the potential to enhance trading strategies and improve overall performance.
Its accessibility and ease of use make it particularly appealing to novice traders, while its adaptability and reliability ensure that it remains a valuable tool for more experienced market participants. As markets continue to evolve, the Uptrick: Trend SMA Oscillator remains a timeless tool, rooted in the fundamental principles of technical analysis, yet flexible enough to meet the demands of modern trading.
[GYTS-CE] Signal Provider | WaveTrend 4D with QMCWaveTrend 4D with Quantile Median Crosses (Community Edition)
🌸 " 📡 Signal Provider" in GoemonYae Trading System (GYTS) 🌸
WaveTrend 4D (WT4D) is an extension of the incredible WaveTrend 3D (2022, Justin Dehorty) . This oscillator elevates the classic WaveTrend by integrating advanced mathematical models for a multi-dimensional view of market momentum, capturing subtle shifts and trends that traditional indicators might miss. Each oscillator layer uses a combination of normalised derivatives, hyperbolic tangent transformations, and dual-pole filtering (John Ehlers' SuperSmoother), providing normalised and smooth signals with minimised lag.
The name "WaveTrend 4D" is derived from the usage of 4 dimensions, representing different frequencies or timeframes. Next to the "fast", "normal" and "slow" frequency, the fourth frequency is called "lethargic" (very slow). This gives the opportunity utilise more dimensions without having abundant signals, since we quantify and filter the quality of signals.
WT4D strives to help discriminating high-quality signals from the indicator by introducing the Gradient Divergence Measure (GDM) and Quantile Median Crosses (QMC). For simplicity, speed and focus, this particular indicator includes only the QMC part. Check the other 🤲Community Edition of this indicator that focuses on the GDM. For QMC, see below for more information.
🌸 --- QUANTILE MEDIAN CROSSES (QMC) --- 🌸
💮 Introduction
--
A powerful approach when working with WaveTrend is to use the frequencies' crossings of the median (zero) line. This would signify a continuation of the reversal. However, not all of those crossings would be trades with a high probability of success. For this reason, we strive to only consider reversals after the most strong trends start to show weakness. We call these reversals the "Quantile Median Crosses" (QMC), deriving the name from the used methodology.
💮 Methodology
--
To find these "most strong trends", we calculate the integral ("the area") of a frequency between all historical median crosses, and take an upper quantile of those integrals. This means that when the frequency is crossing the median in a period of consolidation, the areas between those crosses would be small. But if there was a strong momentum, and the frequency would separate itself significantly from the median and would do so for a long time, its area would be large.
So after considering all the past integrals, we take the upper quantile of those (i.e. sort all integrals and for example take the top 5%) and if the latest trend's integral was in this upper quantile, it is considered "significant". Hence, the name "quantile" in the name "Quantile Median Cross".
💮 QMC on the Oscillator
--
The QMC is shown as a label "🔴" above the median or with "🟢" below the median. The normal frequency has a "bronze" colour, the slow frequency "silver" and the lethargic is "gold". In addition to the labels, there are also diamond shapes in the same colour drawn on the median in the oscillator. This represents the previous median crossing, and helps the user to see between which two points the integral is calculated.
🌸 --- GOEMONYAE TRADING SYSTEM --- 🌸
As previously mentioned, this indicator is a 📡 Signal Provider, part of the suite of the GoemonYae Trading System (🤲 Community Edition). The greatest value comes from connecting multiple 📡 Signal Providers to the 🧬 Flux Composer to find confluence between signals. Contrary to most other indicators that connect with each other, the signals that are passed are not just binary signals ("buy" or "sell") but pass the actual GDM and QMC values. This gives the opportunity in the 🧬 Flux Composer to more accurately use multiple signals with different strengths to finally give an overall signal. On its turn, the Flux Composer can be connected to the GYTS "🎼 Order Orchestrator" for backtesting and trade automation.
Timeframe Continuity Oscillator [TFO]This indicator is used to visualize timeframe continuity - a core concept of "The Strat" - along with some added logic for potential range limiters.
When discussing timeframe continuity, typically we are evaluating several timeframes to see if price is trading above or below the current open of each respective timeframe. If we are concerned with the 15m, 4h, and 1D for example, and price is trading above the current open of each of those timeframes, we can say that we have full timeframe continuity (FTFC) up. Conversely, if price is trading below the current open of each of those timeframes, we can say that we have FTFC down.
We can visualize this with an oscillator of sorts, where the zero line is anchored to the open price of the highest timeframe that we're concerned with. Using the prior example, this would be the 1D timeframe. As long as price is above the current 1D open, it is impossible to have FTFC down; and as long as price is below the current 1D open, it is impossible to have FTFC up. This is why we base the oscillator's values off of the highest timeframe's open (the values are simply how far price has traded from this open) - any value greater than zero tells us that there is potential to have FTFC up, and any value less than zero tells us that there is potential to have FTFC down.
There are a few ways we chose to visualize this data. First, we can choose the "Binary" option which simply uses one solid bullish color above the zero line, and one solid bearish color below the zero line.
Second, we can choose the "Gradient" option to help describe whether we have FTFC up or down. Values above the zero line will be a mix of the bullish color and mid color, where the mid color indicates no timeframe continuity up and the bullish color indicates FTFC up - sort of like a color spectrum of timeframe continuity to describe how many timeframes are in agreement. Similarly, values below the zero line will be a mix of the bearish color and the mid color, where the mid color again indicates no timeframe continuity down and the bearish color indicates FTFC down.
Lastly, we can choose the "FTFC Only" option which will only color the histogram bars as bullish if there is FTFC up, or bearish if there is FTFC down.
One more feature that we added is these upper and lower bands that aim to help describe the potential upper and lower limits that price may travel, relative to the highest timeframe's open. This is done by taking the standard deviation of some defined lookback period, for example, 2 standard deviations of the previous 10 weeks, assuming 1W is the highest timeframe enabled.
The concept is similar to that of an ADR (average daily range) as it can be used to estimate maximum range extensions for the largest timeframe. The arrows you see are plotted once the value exceeds either band - alerts can be enabled for these events as well through any alert() function call.
Machine Learning: Multiple Logistic Regression
Multiple Logistic Regression Indicator
The Logistic Regression Indicator for TradingView is a versatile tool that employs multiple logistic regression based on various technical indicators to generate potential buy and sell signals. By utilizing key indicators such as RSI, CCI, DMI, Aroon, EMA, and SuperTrend, the indicator aims to provide a systematic approach to decision-making in financial markets.
How It Works:
Technical Indicators:
The script uses multiple technical indicators such as RSI, CCI, DMI, Aroon, EMA, and SuperTrend as input variables for the logistic regression model.
These indicators are normalized to create categorical variables, providing a consistent scale for the model.
Logistic Regression:
The logistic regression function is applied to the normalized input variables (x1 to x6) with user-defined coefficients (b0 to b6).
The logistic regression model predicts the probability of a binary outcome, with values closer to 1 indicating a bullish signal and values closer to 0 indicating a bearish signal.
Loss Function (Cross-Entropy Loss):
The cross-entropy loss function is calculated to quantify the difference between the predicted probability and the actual outcome.
The goal is to minimize this loss, which essentially measures the model's accuracy.
// Error Function (cross-entropy loss)
loss(y, p) =>
-y * math.log(p) - (1 - y) * math.log(1 - p)
// y - depended variable
// p - multiple logistic regression
Gradient Descent:
Gradient descent is an optimization algorithm used to minimize the loss function by adjusting the weights of the logistic regression model.
The script iteratively updates the weights (b1 to b6) based on the negative gradient of the loss function with respect to each weight.
// Adjusting model weights using gradient descent
b1 -= lr * (p + loss) * x1
b2 -= lr * (p + loss) * x2
b3 -= lr * (p + loss) * x3
b4 -= lr * (p + loss) * x4
b5 -= lr * (p + loss) * x5
b6 -= lr * (p + loss) * x6
// lr - learning rate or step of learning
// p - multiple logistic regression
// x_n - variables
Learning Rate:
The learning rate (lr) determines the step size in the weight adjustment process. It prevents the algorithm from overshooting the minimum of the loss function.
Users can set the learning rate to control the speed and stability of the optimization process.
Visualization:
The script visualizes the output of the logistic regression model by coloring the SMA.
Arrows are plotted at crossover and crossunder points, indicating potential buy and sell signals.
Lables are showing logistic regression values from 1 to 0 above and below bars
Table Display:
A table is displayed on the chart, providing real-time information about the input variables, their values, and the learned coefficients.
This allows traders to monitor the model's interpretation of the technical indicators and observe how the coefficients change over time.
How to Use:
Parameter Adjustment:
Users can adjust the length of technical indicators (rsi_length, cci_length, etc.) and the Z score length based on their preference and market characteristics.
Set the initial values for the regression coefficients (b0 to b6) and the learning rate (lr) according to your trading strategy.
Signal Interpretation:
Buy signals are indicated by an upward arrow (▲), and sell signals are indicated by a downward arrow (▼).
The color-coded SMA provides a visual representation of the logistic regression output by color.
Table Information:
Monitor the table for real-time information on the input variables, their values, and the learned coefficients.
Keep an eye on the learning rate to ensure a balance between model adjustment speed and stability.
Backtesting and Validation:
Before using the script in live trading, conduct thorough backtesting to evaluate its performance under different market conditions.
Validate the model against historical data to ensure its reliability.
Trend Direction & Levels IdentifierOverview : Trend Direction & Levels Identifier (TDLI) provides you with two lines - Resistance/Support line (RSLine) and Trend Line. These two lines form a channel which is filled with a colour showing current market direction, which also prints Bullish or Bearish text. Trend Line calculation is similar but follows different approach than Super Trend indicator. RSLine calculation is done using EMA and dynamic ATR.
How does this work?
Firstly understand Supertrend - The Supertrend indicator is a freely available technical analysis tool that helps traders identify the direction of the trend . It is based on the concept of volatility, and it provides a simple way to identify whether the current market trend is bullish or bearish.
Here's a basic explanation of the Supertrend indicator's logic and how it is commonly used:
Supertrend Indicator Logic:
Calculation of Average True Range (ATR) : The first step involves calculating the Average True Range (ATR) over a specified period. ATR measures market volatility by considering the average range between the high and low prices over a given number of periods.
Multiplier Factor : A multiplier factor is then applied to the ATR. The multiplier is usually set by the trader or analyst and determines the sensitivity of the Supertrend to changes in volatility.
Calculation of Upper and Lower Bands:The Supertrend indicator calculates two bands - an upper band and a lower band.
Upper Band (UB) = High price - (Multiplier * ATR)
Lower Band (LB) = Low price + (Multiplier * ATR)
Determining Trend Direction : If the current market price is above the Upper Band, the Supertrend suggests a bearish trend (sell signal). If the current market price is below the Lower Band, the Supertrend suggests a bullish trend (buy signal).
Now, Let's understand how we use this logic with some modification to build our Trend line -
Let's break down the key differences:
1. Calculation of Trend Switch Points:
- Supertrend: The Supertrend indicator primarily relies on the Average True Range (ATR) to calculate the volatility of the market. It then determines trend direction based on whether the closing price is above or below the Supertrend line.
- Our Trend: We use a modified ATR for volatility measurement (ATR / x), our code introduces modifications in the calculation of trend switch points. It incorporates moving averages (SMA - Simple Moving Averages) to define high and low prices, adding a dynamic element to the identification of trend reversal points.
2. Trend States and Switch Logic:
- Supertrend: The Supertrend generally has two states: uptrend or downtrend. It switches its state when the closing price crosses the Supertrend line.
- Our Trend: Our code introduces an additional variable, which is not binary (0 or 1) but rather represents the state of the trend (0 for uptrend, 1 for downtrend). The indicator uses a more complex logic involving previous trend states and moving averages to determine trend switches.
So, our trend line incorporates additional elements such as moving averages, dynamic amplitude, and channel deviation to modify the Supertrend logic and provide a more nuanced and visually informative representation of market trends. These modifications offer traders more flexibility in adapting the indicator to different market conditions and trading preferences.
Remember the underlying logic is of Supertrend which is freely available to all.
Another line is RSLine, lets dive into its logic and calculation -
Average True Range (ATR) Calculation : Calculates the Average True Range, a measure of market volatility. The ATR can be dynamically adjusted based on user preference.
Chande Momentum Oscillator (CMO) and Variable (VAR) Calculation : Calculates the CMO, which measures momentum, and uses it to compute the VAR value. This introduces an adaptive element to the indicator.
Other Moving Averages : Calculates various moving averages, including Wilder's Moving Average (WWMA), Zero-Lag Exponential Moving Average (ZLEMA), and Time Series Forecast (TSF), providing different perspectives on trend direction.
Main Moving Average (MAvg) Calculation : Computes the main moving average based on EMA and length.
Stop Level Calculation : Determines stop levels for both long and short positions. The levels are influenced by the moving average (MAvg) and ATR, with an option to normalize them.
The Stop Levels forms the RSLine which acts as either resistance or support based on market direction.
Lets see how the indicator tells you probable market direction -
Direction Identification : Identifies the current trend direction (uptrend or downtrend) based on the relationship between the moving average and the previous stop level. It also prints Bullish or Bearish on chart based on crossovers and crossunders between the Trend Line and the RSLine.
Fill Coloring for Highlighting : It Fills the area between the Trend Line and RSLine with either green or red color to visually emphasize the trend direction. The colors change based on whether the Trend Line and MAvg is above or below the stop levels.
So there are 3 major things -
1. RSLine - Uses EMA and dynamic ATR to calculate stop levels. This acts as support or resistance to current trend. It is always red in colour.
2. Trend Line - Unlike Super Trend this Trend Line calculation uses a combination of highest high, lowest low, and EMA of a fixed range of candles to determine trend changes. It uses a fixed amplitude for calculating the highest high, lowest low, and EMA values, but it doesn't incorporate dynamic volatility adjustments like ATR. Its colour varies from red to green based on calculation.
3. Channel Colour - Channel colour is decided based on crossover of Trend Line and RSLine, if Trend Line crosses RSLine from bottom then channel colour becomes green, similarly red colour is calculated.
How to use this?
Refer this snapshot for content below -
1. Once a crossover happens between Trend Line and RSLine, bullish / bearish text is printed with change in colour of channel. RSLine acts as support/resistance.
2. Look for colour of Trend Line - when it matches channel colour, it means favourable direction is that colour (green - long, red - short)
3. Remember any ongoing trend can reverse any second, so follow price action for better results.
Preferred Timeframe : It works best in 5 minute timeframe, but can also be used in other time frames.
Reason to use these two lines ?
The Trend Line tells current trend direction using a line which keeps changing colours, for double confirmation we use the RSline and channel colour which is calculated using Trend line and RSLine crossover. When both Trend line and RSLine channel colour is same that gives a more solid confirmation (not 100%) of a trend
Why it is worth paying for :
As mentioned earlier this indicator is built over freely available Supertrend and EMA indicators. The modifications which we have done for better calculation and visualisation makes it worth.
The indicator may be considered valuable for traders who appreciate a visual representation of market trend direction and important stop levels. Normal indicators like supertrend just shows a line which gives you idea about the trend but our indicator apart from telling trend direction tells important levels and provide a channel filled with current trend direction significance which helps in following trend precisely.
1. The customization options and visual clarity could enhance decision-making for those who prefer a more tailored approach.
2. Traders willing to pay for this indicator may find it useful in complementing their existing analysis and strategy.
Although one should understand using premium indicator doesnt mean it will generate magic results, if you know price action and risk management properly then only consider trying our indicator else practice trading on free indicators first.
IMPORTANT : As Stock markets are dynamic in nature, no indicator is a magic indicator which will give you 100% accurate results on one click. You still have to use price action for best results.
DISCLAIMER : This indicator isn't a get rich quick scheme, neither it can provide 100% accurate results. It is meant to be used as an aid to Price Action Trading and proper risk management.
BTC Supply in Profits and Losses (BTCSPL) [AlgoAlpha]Description:
🚨The BTC Supply in Profits and Losses (BTCSPL) indicator, developed by AlgoAlpha, offers traders insights into the distribution of INDEX:BTCUSD addresses between profits and losses based on INDEX:BTCUSD on-chain data.
Features:
🔶Alpha Decay Adjustment: The indicator provides the option to adjust the data against Alpha Decay, this compensates for the reduction in clarity of the signal over time.
🔶Rolling Change Display: The indicator enables the display of the rolling change in the distribution of Bitcoin addresses between profits and losses, aiding in identifying shifts in market sentiment.
🔶BTCSPL Value Score: The indicator optionally displays a value score ranging from -1 to 1, traders can use this to carry out strategic dollar cost averaging and reverse dollar cost averaging based on the implied value of bitcoin.
🔶Reversal Signals: The indicator gives long-term reversal signals denoted as "▲" and "▼" for the price of bitcoin based on oversold and overbought conditions of the BTCSPL.
🔶Moving Average Visualization: Traders can choose to display a moving average line, allowing for better trend identification.
How to Use ☝️ (summary):
Alpha Decay Adjustment: Toggle this option to enable or disable Alpha Decay adjustment for a normalized representation of the data.
Moving Average: Toggle this option to show or hide the moving average line, helping traders identify trends.
Short-Term Trend: Enable this option to display the short-term trend based on the Aroon indicator.
Rolling Change: Choose this option to visualize the rolling change in the distribution between profits and losses.
BTCSPL Value Score: Activate this option to show the BTCSPL value score, ranging from -1 to 1, 1 implies that bitcoin is extremely cheap(buy) and -1 implies bitcoin is extremely expensive(sell).
Reversal Signals: Gives binary buy and sell signals for the long term
IU Probability CalculatorHow This Script Works:
1. This script calculate the probability of price reaching a user-defined price level within one candle with the help Normal Distribution Probability Table.
2. Normal Distribution Probability Table is use for calculating probability of events, it's very powerful for calculation of probability and this script is fully based on that table.
3. It takes the Average True Range value or Standard Deviation value of past user-defined length bar.
4. After that it take this formula z = ( price_level - close ) / (ATR or Standard Deviation) and return the value for z, for the bearish side it take z = (close - price level) / (ATR or Standard Deviation ) formula.
5. Once we have the z it look into Normal Distribution Probability Table and match the value.
6. Now the value of z is multiple buy 100 in order to make it look in percentage term.
7. After that this script subtract the final value with 100 because probability always comes under 100%
8. finally we plot the probability at the bottom of the chart the red line indicates "The probability of price not reaching that price level", While the green line indicates "Probability of price Reaching that level " .
9. This script will work fine for both of the directions
How This Is Useful For The User:
1. With this script user can know the probability of price reaching the certain level within one candle for both Directions .
2. This is useful while creating options hedging strategies
3. This can be helpful for deciding stop loss level.
4. It's useful for scalpers for managing their traders and it can be use by binary option traders.
