Rider Algo 5 & 6 Strategies - RSI Extreme Trading [Rider Algo]Rider Algo 5 & 6 Strategies – RSI Extreme Trading
This script combines two of my favorite RSI concepts into a single price-based framework:
Strategy 5 (S5): Extreme Continuation
Strategy 6 (S6): Strength & Weakness Reversal
Everything is plotted directly on the price chart using an “inverse RSI” model that shows where price would be if RSI were at specific levels (default 70/30).
Core Idea – RSI Price Bands
The script builds two dynamic price bands:
Upper RSI Band → price level where RSI = upper level (70)
Lower RSI Band → price level where RSI = lower level (30)
These bands show when the market is operating in RSI overbought/oversold conditions directly on the candles.
Optional markers:
“Exit OB” and “Exit OS” show when price returns inside the band.
Strategy 6 – Strength & Weakness Reversal (S6)
Goal:
Fade exhaustion after a sustained RSI extreme.
Two independent extreme lines:
RSI Extreme Line WEAKNESS (S6) → 70
RSI Extreme Line STRENGTH (S6) → 30
Bearish “Weakness (S6)” signal
RSI trades above the Weakness line for ≥3 bars.
RSI crosses back below.
→ Red Weakness (S6) arrow above price.
Bullish “Strength (S6)” signal
RSI trades below the Strength line for ≥3 bars.
RSI crosses back above.
→ Green Strength (S6) arrow below price.
These are counter-trend reversal setups after extreme RSI stretches.
Strategy 5 – Extreme Continuation (S5)
Goal:
Trade continuation after an extreme breakout, entering on the first clean retest of the extreme line.
Uses:
Same RSI bands/extreme lines
EMA15 as a filter
Long (S5) – Extreme Continuation Long
Price breaks above the upper band.
Price touches EMA15 at least once.
First wick retest of the upper band with close back above → “Long (S5)” with exact entry level.
Short (S5) – Extreme Continuation Short
Mirror logic:
Break below the lower band.
Touch of EMA15.
First wick retest of the lower band with close back below → “Short (S5)”.
Why EMA15 filter?
It forces a cooldown, avoiding rapid-fire continuation signals during a single vertical leg.
Non-repainting logic
All signals use closed bars only.
S6 3-bar regimes use historical bars.
S5 retests are validated after breakout bars close.
EMA15 uses closed candles.
No repainting of historical markers.
Inputs & Customization
Base RSI & Bands
RSI Length – 14
RSI Source – close
Upper/Lower RSI Levels – adjustable (default 70/30)
Strategy 6 – Extreme Reversal
Adjustable Weakness/Strength levels
Toggle signals on/off
Strategy 5 – Extreme Continuation
Toggle Long/Short markers
Optional Exit OB/OS markers
Visual Style
Custom band colors, width, and fill transparency.
Alerts
Master on/off
OB/OS
Band exits
Weakness / Strength
Long (S5) / Short (S5)
How I like to use it
S6 for counter-trend entries after clear extremes.
S5 for continuation when the market is explosive and pulls back to the extreme line after touching EMA15.
Ideas:
Stop-loss beyond the extreme line.
Combine with HTF structure, liquidity or volume.
Works well on assets with expansion characteristics: crypto, indices, FX.
Disclaimer
This script is for educational purposes only.
It is not financial advice.
Test everything in demo and use proper risk management.
Tagging it as:
“Rider Algo 5 & 6 – RSI Extreme Trading”
helps others find it.
Ketidakstabilan
I4I Inside Vortex Strike RateThis indicator identifies what I call an "Inside Vortex": It's similar to a Doji but more strict in having to be inside a keltner and also have a lower ATR than a blended average.
The bar itself is not that special. But it indicates that a potential big move might come in the next 2 periods.
After the patter: It then looks at what I call the Market Maker High and Low: A % of a blended ATR. It then looks back 100-200 or more bars and calculates the overall strike % in history for the High and low after the pattern happens.
This allows us to know how often these levels are hit within the next 2 periods to find if we have any edge on spread, call or put prices or use them as targets.
So its:
Pattern:
Levels
Strike Rate.
Very unique and EXTREME useful. Especially for options traders.
Kaisar Volatility TableMeasures the volatility of an asset or a stock. User can use the lookback input to measure their required volatility. The indicator also provides daily volatility and annualized volatility.
NeuroPolynomial Channel🧠 NeuroPolynomial Channel – AI-Inspired Market Structure Engine
In modern market microstructure analysis, price is no longer treated as a simple line — it is viewed as a continuously evolving signal governed by nonlinear dynamics, volatility deformation, and behavioral state shifts.
The NeuroPolynomial Channel (NPC) is a mathematically structured, AI-inspired indicator designed to approximate this dynamic behavior using a hybrid of:
• Polynomial regression smoothing
• Neural blending functions
• Volatility-adaptive envelopes
• Distribution-based bias levels
While full deep-learning models cannot be directly implemented in Pine Script due to computational and architectural limitations, the NeuroPolynomial Channel brings core AI concepts into TradingView through mathematically constrained approximations, creating an efficient, real-time neural structure model suitable for intraday and swing analysis.
📐 Mathematical Foundation
NPC is not a standard moving average or simple channel system.
It applies a multi-layer non-linear approximation built on four core mathematical components.
1️⃣ NeuroPolynomial Core Line
At the heart of the system lies a recursive polynomial smoothing kernel inspired by neural weighted blending:
K = α · K
+ (1 - α) · P
+ Δx · ( K - K ) / F
Where:
• K = Neuro core estimate
• P = Current price input
• α = Neural morph factor
• F = Flattening constant
• Δx = Position delta (horizontal deformation component)
The recursive references introduce memory similar to RNN-style feedback behavior.
This produces a structurally smooth, non-linear trajectory that adapts to both local and historical price deformation.
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2️⃣ Neural Volatility Envelope
Instead of classical standard deviation, NPC uses a cumulative error field:
E = ( Σ | P - K | ) / N
Using this error field, the dynamic envelope bands are constructed as:
Inner Band = K ± E · m1
Mid Band = K ± E · m2
Outer Band = K ± E · m3
Where:
• m1, m2, m3 are probabilistic band multipliers
• E represents actual observed deviation, not synthetic volatility
This creates a probabilistic price container that deforms with real market behavior rather than static statistical assumptions.
The channel automatically adapts its curvature based on current price regime:
trending, compressing, or expanding.
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3️⃣ Neural Regression Spine
Alongside the polynomial core, NPC calculates a ridge-regularized regression spine:
y = β · x + α (with L2 regularization)
This acts as a structural bias vector or "neural backbone".
It prevents overfitting and provides directional stabilization during extended trend phases.
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4️⃣ Neuro Bias Zones (Daily Reset)
NPC also introduces daily volatility-anchored regime thresholds:
Z_levels = Open ± ATR_daily × {0.1, 0.382, 0.618}
These act as:
• Neuro Mid Zones – equilibrium bands
• Neuro Strong Zones – trend activation boundaries
Unlike classical pivot systems, these levels reset daily and expand dynamically based on real volatility.
They approximate probability field boundaries similar to those used in institutional volatility modeling.
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🤖 AI Philosophy
While Pine Script cannot host full neural networks, GPU models or multi-layer AI pipelines, NeuroPolynomial Channel introduces AI concepts through mathematical abstraction, including:
• Neural blending mechanics
• Memory-based recursion
• Volatility adaptation
• Bias field modeling
• Structured envelope projection
This creates an AI-style behavior using real-time deterministic mathematics — allowing performance on TradingView while preserving interpretability and stability.
🛠 How To Use
NPC is designed for structure-based interpretation, not random signal chasing.
① Trend Structure
Use the Neural Core Line and channel slope to establish trend direction and regime.
② Compression & Expansion
Observe band width.
Contracting channels signal volatility compression.
Expanding channels signal range expansion.
③ Bias Zones
Neuro Mid and Strong levels act as macro intraday bias framework — especially powerful for session trading and index futures.
⚙️ Settings Overview
• Morph Factor – Controls neural blending strength (higher = smoother, lower = reactive)
• Flatten – Reduces polynomial curvature noise
• Band Multipliers – Adjust envelope thickness
• Neural Bias Levels – ATR-anchored regime zones resetting daily
• Theme & Visual Controls – Dark/Light with pro-grade visibility
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Companion AI:
I also built a free Trading AI on ChatGPT that reads chart screenshots and enforces a rule-based intraday checklist.
Use with this indicator: chatgpt.com
For educational & decision-support only. Not financial advice.
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⚠️ Disclaimer
The information contained in my Scripts / Indicators / Ideas / Systems does not constitute financial advice or a solicitation to buy or sell any securities.
All markets carry risk. This tool is for educational and analytical purposes only.
I do not accept liability for any financial loss or damage resulting from direct or indirect use of this script.
Trading decisions must be made independently based on your own risk profile and financial assessment.
Lorentzian Harmonic Flow - Adaptive ML⚡ LORENTZIAN HARMONIC FLOW — ADAPTIVE ML COMPLETE SYSTEM
THEORETICAL FOUNDATION: TEMPORAL RELATIVITY MEETS MACHINE LEARNING
The Lorentzian Harmonic Flow Adaptive ML system represents a paradigm shift in technical analysis by addressing a fundamental limitation that plagues traditional indicators: they assume time flows uniformly across all market conditions. In reality, markets experience time compression during volatile breakouts and time dilation during consolidation. A 50-period moving average calculated during a quiet overnight session captures vastly different market information than the same calculation during a high-volume news event.
This indicator solves this problem through Lorentzian spacetime modeling , borrowed directly from Einstein's special relativity. By calculating a dynamic gamma factor (γ) that measures market velocity relative to a volatility-based "speed of light," every calculation adapts its effective lookback period to the market's intrinsic clock. Combined with a dual-memory architecture, multi-regime detection, and Bayesian strategy selection, this creates a system that genuinely learns which approaches work in which market conditions.
CRITICAL DISTINCTION: TRUE ADAPTIVE LEARNING VS STATIC CLASSIFICATION
Before diving into the system architecture, it's essential to understand how this indicator fundamentally differs from traditional "Lorentzian" implementations, particularly the well-known Lorentzian Classification indicator.
THE ORIGINAL LORENTZIAN CLASSIFICATION APPROACH:
The pioneering Lorentzian Classification indicator (Jdehorty, 2022) introduced the financial community to Lorentzian distance metrics for pattern matching. However, it used offline training methodology :
• External Training: Required Python scripts or external ML tools to train the model on historical data
• Static Model: Once trained, the model parameters remained fixed
• No Real-Time Learning: The indicator classified patterns but didn't learn from outcomes
• Look-Ahead Bias Risk: Offline training could inadvertently use future data
• Manual Retraining: To adapt to new market conditions, users had to retrain externally and reload parameters
This was groundbreaking for bringing ML concepts to Pine Script, but it wasn't truly adaptive. The model was a snapshot—trained once, deployed, static.
THIS SYSTEM: TRUE ONLINE LEARNING
The Lorentzian Harmonic Flow Adaptive ML system represents a complete architectural departure :
✅ FULLY SELF-CONTAINED:
• Zero External Dependencies: No Python scripts, no external training tools, no data exports
• 100% Pine Script: Entire learning pipeline executes within TradingView
• One-Click Deployment: Load indicator, it begins learning immediately
• No Manual Configuration: System builds its own training data in real-time
✅ GENUINE FORWARD-WALK LEARNING:
• Real-Time Adaptation: Every trade outcome updates the model
• Forward-Only Logic: System uses only past confirmed data—zero look-ahead bias
• Continuous Evolution: Parameters adapt bar-by-bar based on rolling performance
• Regime-Specific Memory: Learns which patterns work in which conditions independently
✅ GETS BETTER WITH TIME:
• Week 1: Bootstrap mode—gathering initial data across regimes
• Month 2-3: Statistical significance emerges, parameter adaptation begins
• Month 4+: Mature learning, regime-specific optimization, confident selection
• Year 2+: Deep pattern library, proven parameter sets, robust to regime shifts
✅ NO RETRAINING REQUIRED:
• Automatic Adaptation: When market structure changes, system detects via performance degradation
• Memory Refresh: Old patterns naturally decay, new patterns replace them
• Parameter Evolution: Thresholds and multipliers adjust to current conditions
• Regime Awareness: If new regime emerges, enters bootstrap mode automatically
THE FUNDAMENTAL DIFFERENCE:
Traditional Lorentzian Classification:
"Here are patterns from the past. Current state matches pattern X, which historically preceded move Y. Signal fired."
→ Static knowledge, fixed rules, periodic retraining required
LHF Adaptive ML:
"In Trending Bull regime, Strategy B has 58% win rate and 1.4 Sharpe over last 30 trades. In High Vol Range, Strategy C performs better with 61% win rate and 1.8 Sharpe. Current state is Trending Bull, so I select Strategy B. If Strategy B starts failing, I'll adapt parameters or switch strategies. I'm learning which patterns matter in which contexts, and I improve every trade."
→ Dynamic learning, contextual adaptation, self-improving system
WHY THIS MATTERS:
Markets are non-stationary. A model trained on 2023 data may fail in 2024 when Fed policy shifts, volatility regime changes, or market structure evolves. Static models require constant human intervention—retraining, re-optimization, parameter updates.
This system learns continuously . It doesn't need you to tell it when markets changed. It discovers regime shifts through performance feedback, adapts parameters accordingly, and rebuilds its pattern library organically. The system running in Month 12 is fundamentally smarter than the system in Month 1—not because you retrained it, but because it learned from 1,000+ real outcomes.
This is the difference between pattern recognition (static ML) and reinforcement learning (adaptive ML). One classifies, the other learns and improves.
PART 1: LORENTZIAN TEMPORAL DYNAMICS
Markets don't experience time uniformly. During explosive volatility, price can compress weeks of movement into minutes. During consolidation, time dilates. Traditional indicators ignore this, using fixed periods regardless of market state.
The Lorentzian approach models market time using the Lorentz factor from special relativity:
γ = 1 / √(1 - v²/c²)
Where:
• v (velocity): Trend momentum normalized by ATR, calculated as (close - close ) / (N × ATR)
• c (speed limit): Realized volatility + volatility bursts, multiplied by c_multiplier parameter
• γ (gamma): Time dilation factor that compresses or expands effective lookback periods
When trend velocity approaches the volatility "speed limit," gamma spikes above 1.0, compressing time. Every calculation length becomes: base_period / γ. This creates shorter, more responsive periods during explosive moves and longer, more stable periods during quiet consolidation.
The system raises gamma to an optional power (gamma_power parameter) for fine control over compression strength, then applies this temporal scaling to every calculation in the indicator. This isn't metaphor—it's quantitative adaptation to the market's intrinsic clock.
PART 2: LORENTZIAN KERNEL SMOOTHING
Traditional moving averages use uniform weights (SMA) or exponential decay (EMA). The Lorentzian kernel uses heavy-tailed weighting:
K(distance, γ) = 1 / (1 + (distance/γ)²)
This Cauchy-like distribution gives more influence to recent extremes than Gaussian assumptions suggest, capturing the fat-tailed nature of financial returns. For any calculation requiring smoothing, the system loops through historical bars, computes Lorentzian kernel weights based on temporal distance and current gamma, then produces weighted averages.
This creates adaptive smoothing that responds to local volatility structure rather than imposing rigid assumptions about price distribution.
PART 3: HARMONIC FLOW (Multi-Timeframe Momentum)
The core directional signal comes from Harmonic Flow (HFL) , which blends three gamma-compressed Lorentzian smooths:
• Short Horizon: base_period × short_ratio / γ (default: 34 × 0.5 / γ ≈ 17 bars, faster with high γ)
• Mid Horizon: base_period × mid_ratio / γ (default: 34 × 1.0 / γ ≈ 34 bars, anchor timeframe)
• Long Horizon: base_period × long_ratio / γ (default: 34 × 2.5 / γ ≈ 85 bars, structural trend)
Each produces a Lorentzian-weighted smooth, converted to a z-score (distance from smooth normalized by ATR). These z-scores are then weighted-averaged:
HFL = (w_short × z_short + w_mid × z_mid + w_long × z_long) / (w_short + w_mid + w_long)
Default weights (0.45, 0.35, 0.20) favor recent momentum while respecting longer structure. Scalpers can increase short weight; swing traders can emphasize long weight. The result is a directional momentum indicator that captures multi-timeframe flow in compressed time.
From HFL, the system derives:
• Flow Velocity: HFL - HFL (momentum acceleration)
• Flow Acceleration: Second derivative (turning points)
• Temporal Compression Index (TCI): base_period / compressed_length (shows how much time is compressed)
PART 4: DUAL MEMORY ARCHITECTURE
Markets have memory—current conditions resonate with past regimes. But memory operates on two timescales, inspiring this indicator's dual-memory design:
SHORT-TERM MEMORY (STM):
• Capacity: 100 patterns (configurable 50-200)
• Decay Rate: 0.980 (50% weight after ~35 bars)
• Update Frequency: Every 10 bars
• Purpose: Capture current regime's tactical patterns
• Storage: Recent market states with 10-bar forward outcomes
• Analogy: Hippocampus (rapid encoding, fast fade)
LONG-TERM MEMORY (LTM):
• Capacity: 512 patterns (configurable 256-1024)
• Decay Rate: 0.997 (50% weight after ~230 bars)
• Quality Gate: Only high-quality patterns admitted (adaptive threshold per regime)
• Purpose: Strategic pattern library validated across regimes
• Storage: Validated patterns from weeks/months of history
• Analogy: Neocortex (slow consolidation, persistent storage)
Each memory stores 6-dimensional feature vectors:
1. HFL (harmonic flow strength)
2. Flow Velocity (momentum)
3. Flow Acceleration (turning points)
4. Volatility (realized vol EMA)
5. Entropy (market uncertainty)
6. Gamma (time compression state)
Plus the actual outcome (10-bar forward return).
K-NEAREST NEIGHBORS (KNN) PATTERN MATCHING:
When evaluating current market state, the system queries both memories using Lorentzian distance :
distance = Σ (1 - K(|feature_current - feature_memory|, γ))
This calculates similarity across all 6 dimensions using the same Lorentzian kernel, weighted by current gamma. The system finds K nearest neighbors (default: 8), weights each by:
• Similarity: Lorentzian kernel distance
• Age: Exponential decay based on bars since pattern
• Regime: Only patterns from similar regimes count
The weighted average of these neighbors' outcomes becomes the prediction. High-confidence predictions require both high similarity and agreement between multiple neighbors.
REGIME-AWARE BLENDING:
STM and LTM predictions are blended adaptively:
• High Vol Range regime: Trust STM 70% (recent matters in chaos)
• Trending regimes: Trust LTM 70% (structure matters in trends)
• Normal regimes: 50/50 blend
Agreement metric: When STM and LTM strongly disagree, the system flags low confidence—often indicating regime transition or novel market conditions requiring caution.
PART 5: FIVE-REGIME MARKET CLASSIFICATION
Traditional regime detection stops at "trending vs ranging." This system detects five distinct market states using linear regression slope and volatility analysis:
REGIME 0: TRENDING BULL ↗
• Detection: LR slope > trend_threshold (default: 0.3)
• Characteristics: Sustained positive HFL, elevated gamma, low entropy
• Best Strategy: B (Flow Momentum)
• Trading Behavior: Follow momentum, trail stops, pyramid winners
REGIME 1: TRENDING BEAR ↘
• Detection: LR slope < -trend_threshold
• Characteristics: Sustained negative HFL, elevated gamma, low entropy
• Best Strategy: B (Flow Momentum)
• Trading Behavior: Follow momentum short, aggressive exits on reversal
REGIME 2: HIGH VOL RANGE ↔
• Detection: |slope| < threshold AND vol_ratio > vol_expansion_threshold (default: 1.5)
• Characteristics: Oscillating HFL, high gamma spikes, high entropy
• Best Strategies: A (Squeeze Breakout) or C (Memory Pattern)
• Trading Behavior: Fade extremes, tight stops, quick profits
REGIME 3: LOW VOL RANGE —
• Detection: |slope| < threshold AND vol_ratio < vol_expansion_threshold
• Characteristics: Low HFL magnitude, gamma ≈ 1, squeeze conditions
• Best Strategy: A (Squeeze Breakout)
• Trading Behavior: Wait for breakout, wide stops on breakout entry
REGIME 4: TRANSITION ⚡
• Detection: Trend reversal OR volatility spike > 1.5× threshold
• Characteristics: Erratic gamma, high entropy, conflicting signals
• Best Strategy: None (often unfavorable)
• Trading Behavior: Stand aside, wait for clarity
Each regime gets a confidence score (0-1) measuring how clearly defined it is. Low confidence indicates messy, ambiguous conditions.
PART 6: THREE INDEPENDENT TRADING STRATEGIES
Rather than one signal logic, the system implements three distinct approaches:
STRATEGY A: SQUEEZE BREAKOUT
• Logic: Bollinger Bands squeeze release + HFL direction + flow velocity confirmation
• Calculation: Compares BB width to Keltner Channel width; fires when BB expands beyond KC
• Strength Score: 70 + compression_strength × 0.3 (tighter squeeze = higher score)
• Best Regimes: Low Vol Range (3), Transition exit (4→0 or 4→1)
• Pattern: Volatility contraction → directional expansion
• Philosophy: Calm before the storm; compression precedes explosion
STRATEGY B: LORENTZIAN FLOW MOMENTUM
• Logic: Strong HFL (×flow_mult) + positive velocity + gamma > 1.1 + NOT squeezing
• Calculation: |HFL × flow_mult| > 0.12, velocity confirms direction, gamma shows acceleration
• Strength Score: |HFL × flow_mult| × 80 + gamma × 10
• Best Regimes: Trending Bull (0), Trending Bear (1)
• Pattern: Established momentum → acceleration in compressed time
• Philosophy: Trend is friend when spacetime curves
STRATEGY C: MEMORY PATTERN MATCHING
• Logic: Dual KNN prediction > threshold + high confidence + agreement + HFL confirms
• Calculation: |memory_pred| > 0.005, memory_conf > 1.0, agreement > 0.5, HFL direction matches
• Strength Score: |prediction| × 800 × agreement
• Best Regimes: High Vol Range (2), sometimes others with sufficient pattern library
• Pattern: Historical similarity → outcome resonance
• Philosophy: Markets rhyme; learn from validated patterns
Each strategy generates independent strength scores. In multi-strategy mode (enabled by default), the system selects one strategy per regime based on risk-adjusted performance. In weighted mode (multi-strategy disabled), all three fire simultaneously with configurable weights.
PART 7: ADAPTIVE LEARNING & BAYESIAN SELECTION
This is where machine learning meets trading. The system maintains 15 independent performance matrices :
3 strategies × 5 regimes = 15 tracking systems
For each combination, it tracks:
• Trade Count: Number of completed trades
• Win Count: Profitable outcomes
• Total Return: Sum of percentage returns
• Squared Returns: For variance/Sharpe calculation
• Equity Curve: Virtual P&L assuming 10% risk per trade
• Peak Equity: All-time high for drawdown calculation
• Max Drawdown: Peak-to-trough decline
RISK-ADJUSTED SCORING:
For current regime, the system scores each strategy:
Sharpe Ratio: (mean_return / std_dev) × √252
Calmar Ratio: total_return / max_drawdown
Win Rate: wins / trades
Combined Score = 0.6 × Sharpe + 0.3 × Calmar + 0.1 × Win_Rate
The strategy with highest score is selected. This is similar to Thompson Sampling (multi-armed bandits) but uses deterministic selection rather than probabilistic sampling due to Pine Script limitations.
BOOTSTRAP MODE (Critical for Understanding):
For the first min_regime_samples trades (default: 10) in each regime:
• Status: "🔥 BOOTSTRAP (X/10)" displayed in dashboard
• Behavior: All signals allowed (gathering data)
• Regime Filter: Disabled (can't judge with insufficient data)
• Purpose: Avoid cold-start problem, build statistical foundation
After reaching threshold:
• Status: "✅ FAVORABLE" (score > 0.5) or "⚠️ UNFAVORABLE" (score ≤ 0.5)
• Behavior: Only trade favorable regimes (if enable_regime_filter = true)
• Learning: Parameters adapt based on outcomes
This solves a critical problem: you can't know which strategy works in a regime without data, but you can't get data without trading. Bootstrap mode gathers initial data safely, then switches to selective mode once statistical confidence emerges.
PARAMETER ADAPTATION (Per Regime):
Three parameters adapt independently for each regime based on outcomes:
1. SIGNAL QUALITY THRESHOLD (30-90):
• Starts: base_quality_threshold (default: 60)
• Adaptation:
Win Rate < 45% → RAISE threshold by learning_rate × 10 (be pickier)
Win Rate > 55% → LOWER threshold by learning_rate × 5 (take more)
• Effect: System becomes more selective in losing regimes, more aggressive in winning regimes
2. LTM QUALITY GATE (0.2-0.8):
• Starts: 0.4 (if adaptive gate enabled)
• Adaptation:
Sharpe < 0.5 → RAISE gate by learning_rate (demand better patterns)
Sharpe > 1.5 → LOWER gate by learning_rate × 0.5 (accept more patterns)
• Effect: LTM fills with high-quality patterns from winning regimes
3. FLOW MULTIPLIER (0.5-2.0):
• Starts: 1.0
• Adaptation:
Strong win (+2%+) → MULTIPLY by (1 + learning_rate × 0.1)
Strong loss (-2%+) → MULTIPLY by (1 - learning_rate × 0.1)
• Effect: Amplifies signal strength in profitable regimes, dampens in unprofitable
Each regime evolves independently. Trending Bull might develop threshold=55, gate=0.35, mult=1.3 while High Vol Range develops threshold=70, gate=0.50, mult=0.9.
PART 8: SHADOW PORTFOLIO VALIDATION
To validate learning objectively, the system runs three virtual portfolios :
Shadow Portfolio A: Trades only Strategy A signals
Shadow Portfolio B: Trades only Strategy B signals
Shadow Portfolio C: Trades only Strategy C signals
When any signal fires:
1. Open virtual position for corresponding strategy
2. On exit, calculate P&L (10% risk per trade)
3. Update equity, win count, profit factor
Dashboard displays:
• Equity: Current virtual balance (starts $10,000)
• Win%: Overall win rate across all regimes
• PF: Profit Factor (gross_profit / gross_loss)
This transparency shows which strategies actually perform, validates the selection logic, and prevents overfitting. If Shadow C shows $12,500 equity while A and B show $9,800, it confirms Strategy C's edge.
PART 9: HISTORICAL PRE-TRAINING
The system includes historical pre-training to avoid cold-start:
On Chart Load (if enabled):
1. Scan past pretrain_bars (default: 200)
2. Calculate historical HFL, gamma, velocity, acceleration, volatility, entropy
3. Compute 10-bar forward returns as outcomes
4. Populate STM with recent patterns
5. Populate LTM with high-quality patterns (quality > 0.4)
Effect:
• Without pre-training: Memories empty, no predictions for weeks, pure bootstrap
• With pre-training: System starts with pattern library, predictions from day one
Pre-training uses only past data (no future peeking) and fills memories with validated outcomes. This dramatically accelerates learning without compromising integrity.
PART 10: COMPREHENSIVE INPUT SYSTEM
The indicator provides 50+ inputs organized into logical groups. Here are the key parameters and their market-specific guidance:
🧠 ADAPTIVE LEARNING SYSTEM:
Enable Adaptive Learning (true/false):
• Function: Master switch for regime-specific strategy selection and parameter adaptation
• Enabled: System learns which strategies work in which regimes (recommended)
• Disabled: All strategies fire simultaneously with fixed weights (simpler, less adaptive)
• Recommendation: Keep enabled for all markets; system needs 2-3 months to mature
Learning Rate (0.01-0.20):
• Function: Speed of parameter adaptation based on outcomes
• Stocks/ETFs: 0.03-0.05 (slower, more stable)
• Crypto: 0.05-0.08 (faster, adapts to volatility)
• Forex: 0.04-0.06 (moderate)
• Timeframes:
1-5min scalping: 0.08-0.10 (rapid adaptation)
15min-1H day trading: 0.05-0.07 (balanced)
4H-Daily swing: 0.03-0.05 (conservative)
• Tradeoff: Higher = responsive but may overfit; Lower = stable but slower to adapt
Min Samples Per Regime (5-30):
• Function: Trades required before exiting bootstrap mode
• Active trading (>5 signals/day): 8-10 trades
• Moderate (1-5 signals/day): 10-15 trades
• Swing (few signals/week): 5-8 trades
• Logic: Bootstrap mode until this threshold; then uses Sharpe/Calmar for regime filtering
• Tradeoff: Lower = faster exit (risky, less data); Higher = more validation (safer, slower)
🌍 REGIME DETECTION:
Regime Lookback Period (20-200):
• Function: Bars used for linear regression to classify regime
• By Timeframe:
1-5min: 30-50 bars (~2-4 hour context)
15min: 40-60 bars (daily context)
1H: 50-100 bars (weekly context)
4H: 100-150 bars (monthly context)
Daily: 50-75 bars (quarterly context)
• By Market:
Crypto: 40-60 (faster regime changes)
Forex: 50-75 (moderate stability)
Stocks: 60-100 (slower structural trends)
• Tradeoff: Shorter = more regime switches (reactive); Longer = fewer switches (stable)
Trend Strength Threshold (0.1-0.8):
• Function: Minimum normalized LR slope to classify as trending vs ranging
• Lower (0.1-0.2): More markets classified as trending
• Higher (0.4-0.6): Only strong trends qualify
• Recommendations:
Choppy markets (BTC, small caps): 0.25-0.35
Smooth trends (major FX pairs): 0.30-0.40
Strong trends (indices during bull): 0.20-0.30
• Effect: Controls sensitivity of trending vs ranging classification
Vol Expansion Factor (1.2-3.0):
• Function: Volatility ratio to classify high-vol regimes (current_vol / avg_vol)
• By Asset:
Bitcoin: 1.4-1.6 (frequent vol spikes)
Altcoins: 1.3-1.5 (very volatile)
Major FX (EUR/USD): 1.6-2.0 (stable baseline)
Stocks (SPY): 1.5-1.8 (moderate)
Penny stocks: 1.3-1.4 (always volatile)
• Impact: Higher = fewer "High Vol Range" classifications; Lower = more sensitive to volatility spikes
🎯 SIGNAL GENERATION:
Base Quality Threshold (30-90):
• Function: Starting signal strength requirement (adapts per regime)
• THIS IS YOUR MAIN SIGNAL FREQUENCY CONTROL
• Conservative (70-80): Fewer, higher-quality signals
• Balanced (55-65): Moderate signal flow
• Aggressive (40-50): More signals, more noise
• By Trading Style:
Scalping (1-5min): 50-60
Day trading (15min-1H): 60-70
Swing (4H-Daily): 65-75
• Adaptive Behavior: System raises this in losing regimes (pickier), lowers in winning regimes (take more)
Min Confidence (0.1-0.9):
• Function: Minimum confidence score to fire signal
• Calculation: (Signal_Strength / 100) × Regime_Confidence
• Recommendations:
High-frequency (scalping): 0.2-0.3 (permissive)
Day trading: 0.3-0.4 (balanced)
Swing/position: 0.4-0.6 (selective)
• Interaction: During Transition regime (low regime confidence), even strong signals may fail confidence check; creates natural regime filtering
Only Trade Favorable Regimes (true/false):
• Function: Block signals in unfavorable regimes (where all strategies have negative risk-adjusted scores)
• Enabled (Recommended): Only trades when best strategy has positive Sharpe in current regime; auto-disables during bootstrap; protects capital
• Disabled: Always allows signals regardless of historical performance; use for manual regime assessment
• Bootstrap: Auto-allows trading until min_regime_samples reached, then switches to performance-based filtering
Min Bars Between Signals (1-20):
• Function: Prevents signal spam by enforcing minimum spacing
• By Timeframe:
1min: 3-5 bars (3-5 minutes)
5min: 3-6 bars (15-30 minutes)
15min: 4-8 bars (1-2 hours)
1H: 5-10 bars (5-10 hours)
4H: 3-6 bars (12-24 hours)
Daily: 2-5 bars (2-5 days)
• Logic: After signal fires, no new signals for X bars
• Tradeoff: Lower = more reactive (may overtrade); Higher = more patient (may miss reversals)
🌀 LORENTZIAN CORE:
Base Period (10-100):
• Function: Core time period for flow calculation (gets compressed by gamma)
• THIS IS YOUR PRIMARY TIMEFRAME KNOB
• By Timeframe:
1-5min scalping: 20-30 (fast response)
15min-1H day: 30-40 (balanced)
4H swing: 40-55 (smooth)
Daily position: 50-75 (very smooth)
• By Market Character:
Choppy (crypto, small caps): 25-35 (faster)
Smooth (major FX, indices): 35-50 (moderate)
Slow (bonds, utilities): 45-65 (slower)
• Gamma Effect: Actual length = base_period / gamma; High gamma compresses to ~20 bars, low gamma expands to ~50 bars
• Default 34 (Fibonacci) works well across most assets
Velocity Period (5-50):
• Function: Window for trend velocity calculation: (price_now - price ) / (N × ATR)
• By Timeframe:
1-5min scalping: 8-12 (fast momentum)
15min-1H day: 12-18 (balanced)
4H swing: 14-21 (smooth trend)
Daily: 18-30 (structural trend)
• By Market:
Crypto (fast moves): 10-14
Stocks (moderate): 14-20
Forex (smooth): 18-25
• Impact: Feeds into gamma calculation (v/c ratio); shorter = more sensitive to velocity spikes → higher gamma
• Relationship: Typically vel_period ≈ base_period / 2 to 2/3
Speed-of-Market (c) (0.5-3.0):
• Function: "Speed limit" for gamma calculation: c = realized_vol + vol_burst × c_multiplier
• By Asset Volatility:
High vol (BTC, TSLA): 1.0-1.3 (lower c = more compression)
Medium vol (SPY, EUR/USD): 1.3-1.6 (balanced)
Low vol (bonds, utilities): 1.6-2.5 (higher c = less compression)
• What It Does:
Lower c → velocity hits "speed limit" sooner → higher gamma → more compression
Higher c → velocity rarely hits limit → gamma stays near 1 → less adaptation
• Effect on Signals: More compression (low c) = faster regime detection, more responsive; Less compression (high c) = smoother, less adaptive
• Tuning: Start at 1.4; if gamma always ~1.0, lower to 1.0-1.2; if gamma spikes >5 often, raise to 1.6-2.0
Gamma Power (0.5-2.0):
• Function: Exponent applied to gamma: final_gamma = gamma^power
• Compression Strength:
0.5-0.8: Softens compression (gamma 4 → 2)
1.0: Linear (gamma 4 → 4)
1.2-2.0: Amplifies compression (gamma 4 → 16)
• Use Cases:
Reduce power (<1.0) if adaptive lengths swing too wildly or getting whipsawed
Increase power (>1.0) for more aggressive regime adaptation in fast markets
• Most users should leave at 1.0; only adjust if gamma behavior needs tuning
Max Kernel Lookback (20-200):
• Function: Computational limit for Lorentzian smoothing (performance control)
• Recommendations:
Fast PC / simple chart: 80-100
Slow PC / complex chart: 40-60
Mobile / lots of indicators: 30-50
• Impact: Each kernel smoothing loops through this many bars; higher = more accurate but slower
• Default 60 balances accuracy and speed; lower to 40-50 if indicator is slow
🎼 HARMONIC FLOW:
Short Horizon (0.2-1.0):
• Function: Fast timeframe multiplier: short_length = base_period × short_ratio / gamma
• Default: 0.5 (captures 2× faster flow than base)
• By Style:
Scalping: 0.3-0.4 (very fast)
Day trading: 0.4-0.6 (moderate)
Swing: 0.5-0.7 (balanced)
• Effect: Lower = more weight on micro-moves; Higher = smooths out fast fluctuations
Mid Horizon (0.5-2.0):
• Function: Medium timeframe multiplier: mid_length = base_period × mid_ratio / gamma
• Default: 1.0 (equals base_period, anchor timeframe)
• Usually keep at 1.0 unless specific strategy needs fine-tuning
Long Horizon (1.0-5.0):
• Function: Slow timeframe multiplier: long_length = base_period × long_ratio / gamma
• Default: 2.5 (captures trend/structure)
• By Style:
Scalping: 1.5-2.0 (less long-term influence)
Day trading: 2.0-3.0 (balanced)
Swing: 2.5-4.0 (strong trend component)
• Effect: Higher = more emphasis on larger structure; Lower = more reactive to recent price action
Short Weight (0-1):
Mid Weight (0-1):
Long Weight (0-1):
• Function: Relative importance in HFL calculation (should sum to 1.0)
• Defaults: Short: 0.45, Mid: 0.35, Long: 0.20 (day trading balanced)
• Preset Configurations:
SCALPING (fast response):
Short: 0.60, Mid: 0.30, Long: 0.10
DAY TRADING (balanced):
Short: 0.45, Mid: 0.35, Long: 0.20
SWING (trend-following):
Short: 0.25, Mid: 0.35, Long: 0.40
• Effect: More short weight = responsive but noisier; More long weight = smoother but laggier
🧠 DUAL MEMORY SYSTEM:
Enable Pattern Memory (true/false):
• Function: Master switch for KNN pattern matching via dual memory
• Enabled (Recommended): Strategy C (Memory Pattern) can fire; memory predictions influence all strategies; prediction arcs shown; heatmaps available
• Disabled: Only Strategy A and B available; faster performance (less computation); pure technical analysis (no pattern matching)
• Keep enabled for full system capabilities; disable only if CPU-constrained or testing pure flow signals
STM Size (50-200):
• Function: Short-Term Memory capacity (recent pattern storage)
• Characteristics: Fast decay (0.980), captures current regime, updates every 10 bars, tactical pattern matching
• Sizing:
Active markets (crypto): 80-120
Moderate (stocks): 100-150
Slow (bonds): 50-100
• By Timeframe:
1-15min: 60-100 (captures few hours of patterns)
1H: 80-120 (captures days)
4H-Daily: 100-150 (captures weeks/months)
• Tradeoff: More = better recent pattern coverage; Less = faster computation
• Default 100 is solid for most use cases
LTM Size (256-1024):
• Function: Long-Term Memory capacity (validated pattern storage)
• Characteristics: Slow decay (0.997), only high-quality patterns (gated), regime-specific recall, strategic pattern library
• Sizing:
Fast PC: 512-768
Medium PC: 384-512
Slow PC/Mobile: 256-384
• By Data Needs:
High-frequency (lots of patterns): 512-1024
Moderate activity: 384-512
Low-frequency (swing): 256-384
• Performance Impact: Each KNN search loops through entire LTM; 512 = good balance of coverage and speed; if slow, drop to 256-384
• Fills over weeks/months with validated patterns
STM Decay (0.95-0.995):
• Function: Short-Term Memory age decay rate: age_weight = decay^bars_since_pattern
• Decay Rates:
0.950: Aggressive fade (50% weight after 14 bars)
0.970: Moderate fade (50% after 23 bars)
0.980: Balanced (50% after 35 bars)
0.990: Slow fade (50% after 69 bars)
• By Timeframe:
1-5min: 0.95-0.97 (fast markets, old patterns irrelevant)
15min-1H: 0.97-0.98 (balanced)
4H-Daily: 0.98-0.99 (slower decay)
• Philosophy: STM should emphasize RECENT patterns; lower decay = only very recent matters; 0.980 works well for most cases
LTM Decay (0.99-0.999):
• Function: Long-Term Memory age decay rate
• Decay Rates:
0.990: 50% weight after 69 bars
0.995: 50% weight after 138 bars
0.997: 50% weight after 231 bars
0.999: 50% weight after 693 bars
• Philosophy: LTM should retain value for LONG periods; pattern from 6 months ago might still matter
• Usage:
Fast-changing markets: 0.990-0.995
Stable markets: 0.995-0.998
Structural patterns: 0.998-0.999
• Warning: Be careful with very high decay (>0.998); market structure changes, old patterns may mislead
• 0.997 balances long-term memory with regime evolution
K Neighbors (3-21):
• Function: Number of similar patterns to query in KNN search
• By Sample Size:
Small dataset (<100 patterns): 3-5
Medium dataset (100-300): 5-8
Large dataset (300-1000): 8-13
Very large (>1000): 13-21
• Tradeoff:
Fewer K (3-5): More reactive to closest matches; noisier; outlier-sensitive; better when patterns very distinct
More K (13-21): Smoother, more stable predictions; may dilute strong signals; better when patterns overlap
• Rule of Thumb: K ≈ √(memory_size) / 3; For STM=100, LTM=512: K ≈ 8-10 ideal
Adaptive Quality Gate (true/false):
• Function: Adapts LTM entry threshold per regime based on Sharpe ratio
• Enabled: Quality gate adapts: Low Sharpe → RAISE gate (demand better patterns); High Sharpe → LOWER gate (accept more patterns); each regime has independent gate
• Disabled: Fixed quality gate (0.4 default) for all regimes
• Recommended: Keep ENABLED; helps LTM focus on proven pattern types per regime; prevents weak patterns from polluting memory
🎯 MULTI-STRATEGY SYSTEM:
Enable Strategy Learning (true/false):
• Function: Core learning feature for regime-specific strategy selection
• Enabled: Tracks 3 strategies × 5 regimes = 15 performance matrices; selects best strategy per regime via Sharpe/Calmar/WinRate; adaptive strategy switching
• Disabled: All strategies fire simultaneously (weighted combination); no regime-specific selection; simpler but less adaptive
• Recommended: ENABLED (this is the core of the adaptive system); disable only for testing or simplification
Strategy A Weight (0-1):
• Function: Weight for Strategy A (Squeeze Breakout) when multi-strategy disabled
• Characteristics: Fires on Bollinger squeeze release; best in Low Vol Range, Transition; compression → expansion pattern
• When Multi-Strategy OFF: Default 0.33 (equal weight); increase to 0.4-0.5 for choppy ranges with breakouts; decrease to 0.2-0.3 for trending markets
• When Multi-Strategy ON: This is ignored (system auto-selects based on performance)
Strategy B Weight (0-1):
• Function: Weight for Strategy B (Lorentzian Flow) when multi-strategy disabled
• Characteristics: Fires on strong HFL + velocity + gamma; best in Trending Bull/Bear; momentum → acceleration pattern
• When Multi-Strategy OFF: Default 0.33; increase to 0.4-0.5 for trending markets; decrease to 0.2-0.3 for choppy/ranging markets
• When Multi-Strategy ON: Ignored (auto-selected)
Strategy C Weight (0-1):
• Function: Weight for Strategy C (Memory Pattern) when multi-strategy disabled
• Characteristics: Fires when dual KNN predicts strong move; best in High Vol Range; requires memory system enabled + sufficient data
• When Multi-Strategy OFF: Default 0.34; increase to 0.4-0.6 if strong pattern repetition and LTM has >200 patterns; decrease to 0.2-0.3 if new to system; set to 0.0 if memory disabled
• When Multi-Strategy ON: Ignored (auto-selected)
📚 PRE-TRAINING:
Historical Pre-Training (true/false):
• Function: Bootstrap feature that fills memory on chart load
• Enabled: Scans past bars to populate STM/LTM before live trading; calculates historical outcomes (10-bar forward returns); builds initial pattern library; system starts with context, not blank slate
• Disabled: Memories only populate in real-time; takes weeks to build pattern library
• Recommended: ENABLED (critical for avoiding "cold start" problem); disable only for testing clean learning
Training Bars (50-500):
• Function: How many historical bars to scan on load (limited by available history)
• Recommendations:
1-5min charts: 200-300 (few hours of history)
15min-1H: 200-400 (days/weeks)
4H: 300-500 (months)
Daily: 200-400 (years)
• Performance:
100 bars: ~1 second
300 bars: ~2-3 seconds
500 bars: ~4-5 seconds
• Sweet Spot: 200-300 (enough patterns without slow load)
• If chart loads slowly: Reduce to 100-150
🎨 VISUALIZATION:
Show Regime Background (true/false):
• Function: Color-code background by current regime
• Colors: Trending Bull (green tint), Trending Bear (red tint), High Vol Range (orange tint), Low Vol Range (blue tint), Transition (purple tint)
• Helps visually track regime changes
Show Flow Bands (true/false):
• Function: Plot upper/lower bands based on HFL strength
• Shows dynamic support/resistance zones; green fill = bullish flow; red fill = bearish flow
• Useful for visual trend confirmation
Show Confidence Meter (true/false):
• Function: Plot signal confidence (0-100) in separate pane
• Calculation: (Signal_Strength / 100) × Regime_Confidence
• Gold line = current confidence; dashed line = minimum threshold
• Signals fire when confidence exceeds threshold
Show Prediction Arc (true/false):
• Function: Dashed line projecting expected price move based on memory prediction
• NOT a price target - a probability vector; steep arc = strong expected move; flat arc = weak/uncertain prediction
• Green = bullish prediction; red = bearish prediction
Show Signals (true/false):
• Function: Triangle markers at entry points
• ▲ Green = Long signal; ▼ Red = Short signal
• Markers show on bar close (non-repainting)
🏆 DASHBOARD:
Show Dashboard (true/false):
• Function: Main info panel showing all system metrics
• Sections: Lorentzian Core, Regime, Dual Memory, Adaptive Parameters, Regime Performance, Shadow Portfolios, Current Signal Status
• Essential for understanding system state
Dashboard Position: Top Left, Top Right, Bottom Left, Bottom Right
Individual Section Toggles:
• System Stats: Lorentzian Core section (Gamma, v/c, HFL, TCI)
• Memory Stats: Dual Memory section (STM/LTM predictions, agreement)
• Shadow Portfolios: Shadow Portfolio table (equity, win%, PF)
• Adaptive Params: Adaptive Parameters section (threshold, quality gate, flow mult)
🔥 HEATMAPS:
Show Dual Heatmaps (true/false):
• Function: Visual pattern density maps for STM and LTM
• Layout: X-axis = pattern age (left=recent, right=old); Y-axis = outcome direction (top=bearish, bottom=bullish); Color intensity = pattern count; Color hue = bullish (green) vs bearish (red)
• Warning: Can clutter chart; disable if not using
Heatmap Position: Screen position for heatmaps (STM at selected position, LTM offset)
Resolution (5-15):
• Function: Grid resolution (bins)
• Higher = more detailed but smaller cells; Lower = clearer but less granular
• 10 is good balance; reduce to 6-8 if hard to read
PART 11: DASHBOARD METRICS EXPLAINED
The comprehensive dashboard provides real-time transparency into every aspect of the adaptive system:
⚡ LORENTZIAN CORE SECTION:
Gamma (γ):
• Range: 1.0 to ~10.0 (capped)
• Interpretation:
γ ≈ 1.0-1.2: Normal market time, low velocity
γ = 1.5-2.5: Moderate compression, trending
γ = 3.0-5.0: High compression, explosive moves
γ > 5.0: Extreme compression, parabolic volatility
• Usage: High gamma = system operating in compressed time; expect shorter effective periods and faster adaptation
v/c (Velocity / Speed Limit):
• Range: 0.0 to 0.999 (approaches but never reaches 1.0)
• Interpretation:
v/c < 0.3: Slow market, low momentum
v/c = 0.4-0.7: Moderate trending
v/c > 0.7: Approaching "speed limit," high velocity
v/c > 0.9: Parabolic move, system at limit
• Color Coding: Red (>0.7), Gold (0.4-0.7), Green (<0.4)
• Usage: High v/c warns of extreme conditions where trend may exhaust
HFL (Harmonic Flow):
• Range: Typically -3.0 to +3.0 (can exceed in extremes)
• Interpretation:
HFL > 0: Bullish flow
HFL < 0: Bearish flow
|HFL| > 0.5: Strong directional bias
|HFL| < 0.2: Weak, indecisive
• Color: Green (positive), Red (negative)
• Usage: Primary directional indicator; strategies often require HFL confirmation
TCI (Temporal Compression Index):
• Calculation: base_period / compressed_length
• Interpretation:
TCI ≈ 1.0: No compression, normal time
TCI = 1.5-2.5: Moderate compression
TCI > 3.0: Significant compression
• Usage: Shows how much time is being compressed; mirrors gamma but more intuitive
╔═══ REGIME SECTION ═══╗
Current:
• Display: Regime name with icon (Trending Bull ↗, Trending Bear ↘, High Vol Range ↔, Low Vol Range —, Transition ⚡)
• Color: Gold for visibility
• Usage: Know which regime you're in; check regime performance to see expected strategy behavior
Confidence:
• Range: 0-100%
• Interpretation:
>70%: Very clear regime definition
40-70%: Moderate clarity
<40%: Ambiguous, mixed conditions
• Color: Green (>70%), Gold (40-70%), Red (<40%)
• Usage: High confidence = trust regime classification; low confidence = regime may be transitioning
Mode:
• States:
🔥 BOOTSTRAP (X/10): Still gathering data for this regime
✅ FAVORABLE: Best strategy has positive risk-adjusted score (>0.5)
⚠️ UNFAVORABLE: All strategies have negative scores (≤0.5)
• Color: Orange (bootstrap), Green (favorable), Red (unfavorable)
• Critical Importance: This tells you whether the system will trade or stand aside (if regime filter enabled)
╔═══ DUAL MEMORY KNN SECTION ═══╗
STM (Size):
• Display: Number of patterns currently in STM (0 to stm_size)
• Interpretation: Should fill to capacity within hours/days; if not filling, check that memory is enabled
STM Pred:
• Range: Typically -0.05 to +0.05 (representing -5% to +5% expected 10-bar move)
• Color: Green (positive), Red (negative)
• Usage: STM's prediction based on recent patterns; emphasis on current regime
LTM (Size):
• Display: Number of patterns in LTM (0 to ltm_size)
• Interpretation: Fills slowly (weeks/months); only validated high-quality patterns; check quality gate if not filling
LTM Pred:
• Range: Similar to STM pred
• Color: Green (positive), Red (negative)
• Usage: LTM's prediction based on long-term validated patterns; more strategic than tactical
Agreement:
• Display:
✅ XX%: Strong agreement (>70%) - both memories aligned
⚠️ XX%: Moderate agreement (40-70%) - some disagreement
❌ XX%: Conflict (<40%) - memories strongly disagree
• Color: Green (>70%), Gold (40-70%), Red (<40%)
• Critical Usage: Low agreement often precedes regime change or signals novel conditions; Strategy C won't fire with low agreement
╔═══ ADAPTIVE PARAMS SECTION ═══╗
Threshold:
• Display: Current regime's signal quality threshold (30-90)
• Interpretation: Higher = pickier; lower = more permissive
• Watch For: If steadily rising in a regime, system is struggling (low win rate); if falling, system is confident
• Default: Starts at base_quality_threshold (usually 60)
Quality:
• Display: Current regime's LTM quality gate (0.2-0.8)
• Interpretation: Minimum quality score for pattern to enter LTM
• Watch For: If rising, system demanding higher-quality patterns; if falling, accepting more diverse patterns
• Default: Starts at 0.4
Flow Mult:
• Display: Current regime's flow multiplier (0.5-2.0)
• Interpretation: Amplifies or dampens HFL for Strategy B
• Watch For: If >1.2, system found strong edge in flow signals; if <0.8, flow signals underperforming
• Default: Starts at 1.0
Learning:
• Display: ✅ ON or ❌ OFF
• Shows whether adaptive learning is enabled
• Color: Green (on), Red (off)
╔═══ REGIME PERFORMANCE SECTION ═══╗
This table shows ONLY the current regime's statistics:
S (Strategy):
• Display: A, B, or C
• Color: Gold if selected strategy; gray if not
• Shows which strategies have data in this regime
Trades:
• Display: Number of completed trades for this pair
• Interpretation: Blank or low numbers mean bootstrap mode; >10 means statistical significance building
Win%:
• Display: Win rate percentage
• Color: Green (>55%), White (45-55%), Red (<45%)
• Interpretation: 52%+ is good; 58%+ is excellent; <45% means struggling
• Note: Short-term variance is normal; judge after 20+ trades
Sharpe:
• Display: Annualized Sharpe ratio
• Color: Green (>1.0), White (0-1.0), Red (<0)
• Interpretation:
>2.0: Exceptional (rare)
1.0-2.0: Good
0.5-1.0: Acceptable
0-0.5: Marginal
<0: Losing
• Usage: Primary metric for strategy selection (60% weight in score)
╔═══ SHADOW PORTFOLIOS SECTION ═══╗
Shows virtual equity tracking across ALL regimes (not just current):
S (Strategy):
• Display: A, B, or C
• Color: Gold if currently selected strategy; gray otherwise
Equity:
• Display: Current virtual balance (starts $10,000)
• Color: Green (>$10,000), White ($9,500-$10,000), Red (<$9,500)
• Interpretation: Which strategy is actually making virtual money across all conditions
• Note: 10% risk per trade assumed
Win%:
• Display: Overall win rate across all regimes
• Color: Green (>55%), White (45-55%), Red (<45%)
• Interpretation: Aggregate performance; strategy may do well in some regimes and poorly in others
PF (Profit Factor):
• Display: Gross profit / gross loss
• Color: Green (>1.5), White (1.0-1.5), Red (<1.0)
• Interpretation:
>2.0: Excellent
1.5-2.0: Good
1.2-1.5: Acceptable
1.0-1.2: Marginal
<1.0: Losing
• Usage: Confirms win rate; high PF with moderate win rate means winners >> losers
╔═══ STATUS BAR ═══╗
Display States:
• 🟢 LONG: Currently in long position (green background)
• 🔴 SHORT: Currently in short position (red background)
• ⬆️ LONG SIGNAL: Long signal present but not yet confirmed (waiting for bar close)
• ⬇️ SHORT SIGNAL: Short signal present but not yet confirmed
• ⚪ NEUTRAL: No position, no signal (white background)
Usage: Immediate visual confirmation of system state; check before manually entering/exiting
PART 12: VISUAL ELEMENT INTERPRETATION
REGIME BACKGROUND COLORS:
Green Tint: Trending Bull regime - expect Strategy B (Flow) to dominate; focus on long momentum
Red Tint: Trending Bear regime - expect Strategy B (Flow) shorts; focus on short momentum
Orange Tint: High Vol Range - expect Strategy A (Squeeze) or C (Memory); trade breakouts or patterns
Blue Tint: Low Vol Range - expect Strategy A (Squeeze); wait for compression release
Purple Tint: Transition regime - often unfavorable; system may stand aside; high uncertainty
Usage: Quick visual regime identification without reading dashboard
FLOW BANDS:
Upper Band: close + HFL × ATR × 1.5
Lower Band: close - HFL × ATR × 1.5
Green Fill: HFL positive (bullish flow); bands act as dynamic support/resistance in uptrend
Red Fill: HFL negative (bearish flow); bands act as dynamic resistance/support in downtrend
Usage:
• Bullish flow: Price bouncing off lower band = trend continuation; breaking below = possible reversal
• Bearish flow: Price rejecting upper band = trend continuation; breaking above = possible reversal
CONFIDENCE METER (Separate Pane):
Gold Line: Current signal confidence (0-100)
Dashed Line: Minimum confidence threshold
Interpretation:
• Line above threshold: Signal likely to fire if strength sufficient
• Line below threshold: Even if signal logic met, won't fire (insufficient confidence)
• Gradual rise: Signal building strength
• Sharp spike: Sudden conviction (check if sustainable)
Usage: Real-time signal probability; helps anticipate upcoming entries
PREDICTION ARC:
Dashed Line: Projects from current close to expected price 8 bars forward
Green Arc: Bullish memory prediction
Red Arc: Bearish memory prediction
Steep Arc: High conviction (strong expected move)
Flat Arc: Low conviction (weak/uncertain move)
Important: NOT a price target; this is a probability vector based on KNN outcomes; actual price may differ
Usage: Directional bias from pattern matching; confirms or contradicts flow signals
SIGNAL MARKERS:
▲ Green Triangle (below bar):
• Long signal confirmed on bar close
• Entry on next bar open
• Non-repainting (appears after bar closes)
▼ Red Triangle (above bar):
• Short signal confirmed on bar close
• Entry on next bar open
• Non-repainting
Size: Tiny (unobtrusive)
Text: "L" or "S" in marker
Usage: Historical signal record; alerts should fire on these; verify against dashboard status
DUAL HEATMAPS (If Enabled):
STM HEATMAP:
• X-axis: Pattern age (left = recent, right = older, typically 0-50 bars)
• Y-axis: Outcome direction (top = bearish outcomes, bottom = bullish outcomes)
• Color Intensity: Brightness = pattern count in that cell
• Color Hue: Green tint (bullish), Red tint (bearish), Gray (neutral)
Interpretation:
• Dense bottom-left: Many recent bullish patterns (bullish regime)
• Dense top-left: Many recent bearish patterns (bearish regime)
• Scattered: Mixed outcomes, ranging regime
• Empty areas: Few patterns (low data)
LTM HEATMAP:
• Similar layout but X-axis spans wider age range (0-500+ bars)
• Shows long-term pattern distribution
• Denser = more validated patterns
Comparison Usage:
• If STM and LTM heatmaps look similar: Current regime matches historical patterns (high agreement)
• If STM bottom-heavy but LTM top-heavy: Recent bullish activity contradicts historical bearish patterns (low agreement, transition signal)
PART 13: DEVELOPMENT STORY
The creation of the Lorentzian Harmonic Flow Adaptive ML system represents over six months of intensive research, mathematical exploration, and iterative refinement. What began as a theoretical investigation into applying special relativity to market time evolved into a complete adaptive learning framework.
THE CHALLENGE:
The fundamental problem was this: markets don't experience time uniformly, yet every indicator treats a 50-period calculation the same whether markets are exploding or sleeping. Traditional adaptive indicators adjust parameters based on volatility, but this is reactive—by the time you measure high volatility, the explosive move is over. What was needed was a framework that measured the market's intrinsic velocity relative to its own structural limits, then compressed time itself proportionally.
THE LORENTZIAN INSIGHT:
Einstein's special relativity provides exactly this framework through the Lorentz factor. When an object approaches the speed of light, time dilates—but from the object's reference frame, it experiences time compression. By treating price velocity as analogous to relativistic velocity and volatility structure as the "speed limit," we could calculate a gamma factor that compressed lookback periods during explosive moves.
The mathematics were straightforward in theory but devilishly complex in implementation. Pine Script has no native support for dynamically-sized arrays or recursive functions, forcing creative workarounds. The Lorentzian kernel smoothing required nested loops through historical bars, calculating kernel weights on the fly—a computational nightmare. Early versions crashed or produced bizarre artifacts (negative gamma values, infinite loops during volatility spikes).
Optimization took weeks. Limiting kernel lookback to 60 bars while still maintaining smoothing quality. Pre-calculating gamma once per bar and reusing it across all calculations. Caching intermediate results. The final implementation balances mathematical purity with computational reality.
THE MEMORY ARCHITECTURE:
With temporal compression working, the next challenge was pattern memory. Simple moving average systems have no memory—they forget yesterday's patterns immediately. But markets are non-stationary; what worked last month may not work today. The solution: dual-memory architecture inspired by cognitive neuroscience.
Short-Term Memory (STM) would capture tactical patterns—the hippocampus of the system. Fast encoding, fast decay, always current. Long-Term Memory (LTM) would store validated strategic patterns—the neocortex. Slow consolidation, persistent storage, regime-spanning wisdom.
The KNN implementation nearly broke me. Calculating Lorentzian distance across 6 dimensions for 500+ patterns per query, applying age decay, filtering by regime, finding K nearest neighbors without native sorting functions—all while maintaining sub-second execution. The breakthrough came from realizing we could use destructive sorting (marking found neighbors as "infinite distance") rather than maintaining separate data structures.
Pre-training was another beast. To populate memory with historical patterns, the system needed to scan hundreds of past bars, calculate forward outcomes, and insert patterns—all on chart load without timing out. The solution: cap at 200 bars, optimize loops, pre-calculate features. Now it works seamlessly.
THE REGIME DETECTION:
Five-regime classification emerged from empirical observation. Traditional trending/ranging dichotomy missed too much nuance. Markets have at least four distinct states: trending up, trending down, volatile range, quiet range—plus a chaotic transition state. Linear regression slope quantifies trend; volatility ratio quantifies expansion; combining them creates five natural clusters.
But classification is useless without regime-specific learning. That meant tracking 15 separate performance matrices (3 strategies × 5 regimes), computing Sharpe ratios and Calmar ratios for sparse data, implementing Bayesian-like strategy selection. The bootstrap mode logic alone took dozens of iterations—too strict and you never get data, too permissive and you blow up accounts during learning.
THE ADAPTIVE LAYER:
Parameter adaptation was conceptually elegant but practically treacherous. Each regime needed independent thresholds, quality gates, and multipliers that adapted based on outcomes. But naive gradient descent caused oscillations—win a few trades, lower threshold, take worse signals, lose trades, raise threshold, miss good signals. The solution: exponential smoothing via learning rate (α) and separate scoring for selection vs adaptation.
Shadow portfolios provided objective validation. By running virtual accounts for all strategies simultaneously, we could see which would have won even when not selected. This caught numerous bugs where selection logic was sound but execution was flawed, or vice versa.
THE DASHBOARD & VISUALIZATION:
A learning system is useless if users can't understand what it's doing. The dashboard went through five complete redesigns. Early versions were information dumps—too much data, no hierarchy, impossible to scan. The final version uses visual hierarchy (section headers, color coding, strategic whitespace) and progressive disclosure (show current regime first, then performance, then parameters).
The dual heatmaps were a late addition but proved invaluable for pattern visualization. Seeing STM cluster in one corner while LTM distributed broadly immediately signals regime novelty. Traders grasp this visually faster than reading disagreement percentages.
THE TESTING GAUNTLET:
Testing adaptive systems is uniquely challenging. Static backtest results mean nothing—the system should improve over time. Early "tests" showed abysmal performance because bootstrap periods were included. The breakthrough: measure pre-learning baseline vs post-learning performance. A system going from 48% win rate (first 50 trades) to 56% win rate (trades 100-200) is succeeding even if absolute performance seems modest.
Edge cases broke everything repeatedly. What happens when a regime never appears in historical data? When all strategies fail simultaneously? When memory fills with only bearish patterns during a bull run? Each required careful handling—bootstrap modes, forced diversification, quality gates.
THE DOCUMENTATION:
This isn't an indicator you throw on a chart with default settings and trade immediately. It's a learning system that requires understanding. The input tooltips alone contain over 10,000 words of guidance—market-specific recommendations, timeframe-specific settings, tradeoff explanations. Every parameter needed not just a description but a philosophical justification and practical tuning guide.
The code comments span 500+ lines explaining theory, implementation decisions, edge cases. Future maintainers (including myself in six months) need to understand not just what the code does but why certain approaches were chosen over alternatives.
WHAT ALMOST DIDN'T WORK:
The entire project nearly collapsed twice. First, when initial Lorentzian smoothing produced complete noise—hours of debugging revealed a simple indexing error where I was accessing instead of in the kernel loop. One character, entire system broken.
Second, when memory predictions showed zero correlation with outcomes. Turned out the KNN distance metric was dominated by the gamma dimension (values 1-10) drowning out normalized features (values -1 to 1). Solution: apply kernel transformation to all dimensions, not just final distance. Obvious in retrospect, maddening at the time.
THE PHILOSOPHY:
This system embodies a specific philosophy: markets are learnable but non-stationary. No single strategy works forever, but regime-specific patterns persist. Time isn't uniform, memory isn't perfect, prediction isn't possible—but probabilistic edges exist for those willing to track them rigorously.
It rejects the premise that indicators should give universal advice. Instead, it says: "In this regime, based on similar past states, Strategy B has a 58% win rate and 1.4 Sharpe. Strategy A has 45% and 0.2 Sharpe. I recommend B. But we're still in bootstrap for Strategy C, so I'm gathering data. Check back in 5 trades."
That humility—knowing what it knows and what it doesn't—is what makes it robust.
PART 14: PROFESSIONAL USAGE PROTOCOL
PHASE 1: DEPLOYMENT (Week 1-4)
Initial Setup:
1. Load indicator on primary trading chart with default settings
2. Verify historical pre-training enabled (should see ~200 patterns in STM/LTM on first load)
3. Enable all dashboard sections for maximum transparency
4. Set alerts but DO NOT trade real money
Observation Checklist:
• Dashboard Validation:
✓ Lorentzian Core shows reasonable gamma (1-5 range, not stuck at 1.0 or spiking to 10)
✓ HFL oscillates with price action (not flat or random)
✓ Regime classifications make intuitive sense
✓ Confidence scores vary appropriately
• Memory System:
✓ STM fills within first few hours/days of real-time bars
✓ LTM grows gradually (few patterns per day, quality-gated)
✓ Predictions show directional bias (not always 0.0)
✓ Agreement metric fluctuates with regime changes
• Bootstrap Tracking:
✓ Dashboard shows "🔥 BOOTSTRAP (X/10)" for each regime
✓ Trade counts increment on regime-specific signals
✓ Different regimes reach threshold at different rates
Paper Trading:
• Take EVERY signal (ignore unfavorable warnings during bootstrap)
• Log each trade: entry price, regime, selected strategy, outcome
• Calculate your actual P&L assuming proper risk management (1-2% risk per trade)
• Do NOT judge system performance yet—focus on understanding behavior
Troubleshooting:
• No signals for days:
- Check base_quality_threshold (try lowering to 50-55)
- Verify enable_regime_filter not blocking all regimes
- Confirm signal confidence threshold not too high (try 0.25)
• Signals every bar:
- Raise base_quality_threshold to 65-70
- Increase min_bars_between to 8-10
- Check if gamma spiking excessively (raise c_multiplier)
• Memory not filling:
- Confirm enable_memory = true
- Verify historical pre-training completed (check STM size after load)
- May need to wait 10 bars for first real-time update
PHASE 2: VALIDATION (Week 5-12)
Statistical Emergence:
By week 5-8, most regimes should exit bootstrap. Look for:
✓ Regime Performance Clarity:
- At least 2-3 strategies showing positive Sharpe in their favored regimes
- Clear separation (Strategy B strong in Trending, Strategy A strong in Low Vol Range, etc.)
- Win rates stabilizing around 50-60% for winning strategies
✓ Shadow Portfolio Divergence:
- Virtual portfolios showing clear winners ($10K → $11K+) and losers ($10K → $9K-)
- Profit factors >1.3 for top strategy
- System selection aligning with best shadow portfolio
✓ Parameter Adaptation:
- Thresholds varying per regime (not stuck at initial values)
- Quality gates adapting (some regimes higher, some lower)
- Flow multipliers showing regime-specific optimization
Validation Questions:
1. Do patterns make intuitive sense?
- Strategy B (Flow) dominating Trending Bull/Bear? ✓ Expected
- Strategy A (Squeeze) succeeding in Low Vol Range? ✓ Expected
- Strategy C (Memory) working in High Vol Range? ✓ Expected
- Random strategy winning everywhere? ✗ Problem
2. Is unfavorable filtering working?
- Regimes with negative Sharpe showing "⚠️ UNFAVORABLE"? ✓ System protecting capital
- Transition regime often unfavorable? ✓ Expected
- All regimes perpetually unfavorable? ✗ Settings too strict or asset unsuitable
3. Are memories agreeing appropriately?
- High agreement during stable regimes? ✓ Expected
- Low agreement during transitions? ✓ Expected (novel conditions)
- Perpetual conflict? ✗ Check memory sizes or decay rates
Fine-Tuning (If Needed):
Too Many Signals in Losing Regimes:
→ Increase learning_rate to 0.07-0.08 (faster adaptation)
→ Raise base_quality_threshold by 5-10 points
→ Enable regime filter if disabled
Missing Profitable Setups:
→ Lower base_quality_threshold by 5-10 points
→ Reduce min_confidence to 0.25-0.30
→ Check if bootstrap mode blocking trades (let it complete)
Excessive Parameter Swings:
→ Reduce learning_rate to 0.03-0.04
→ Increase min_regime_samples to 15-20 (more data before adaptation)
Memory Disagreement Too Frequent:
→ Increase LTM size to 768-1024 (broader pattern library)
→ Lower adaptive_quality_gate requirement (allow more patterns)
→ Increase K neighbors to 10-12 (smoother predictions)
PHASE 3: LIVE TRADING (Month 4+)
Pre-Launch Checklist:
1. ✓ At least 3 regimes show positive Sharpe (>0.8)
2. ✓ Top shadow portfolio shows >53% win rate and >1.3 profit factor
3. ✓ Parameters have stabilized (not changing more than 10% per month)
4. ✓ You understand every dashboard metric and can explain regime/strategy behavior
5. ✓ You have proper risk management plan independent of this system
Position Sizing:
Conservative (Recommended for Month 4-6):
• Risk per trade: 0.5-1.0% of account
• Max concurrent positions: 1-2
• Total exposure: 10-25% of intended full size
Moderate (Month 7-12):
• Risk per trade: 1.0-1.5% of account
• Max concurrent positions: 2-3
• Total exposure: 25-50% of intended size
Full Scale (Year 2+):
• Risk per trade: 1.5-2.0% of account
• Max concurrent positions: 3-5
• Total exposure: 100% (still following risk limits)
Entry Execution:
On Signal Confirmation:
1. Verify dashboard shows signal type (▲ LONG or ▼ SHORT)
2. Check regime mode (avoid if "⚠️ UNFAVORABLE" unless testing)
3. Note selected strategy (A/B/C) and its regime Sharpe
4. Verify memory agreement if Strategy C selected (want >60%)
Entry Method:
• Market entry: Next bar open after signal (for exact backtest replication)
• Limit entry: Slight improvement (2-3 ticks) if confident in direction
Stop Loss Placement:
• Strategy A (Squeeze): Beyond opposite band or recent swing point
• Strategy B (Flow): 1.5-2.0 ATR from entry against direction
• Strategy C (Memory): Based on predicted move magnitude (tighter if pred > 2%)
Exit Management:
System Exit Signals:
• Opposite signal fires: Immediate exit, potential reversal entry
• 20 bars no exit signal: System implies position stale, consider exiting
• Regime changes to unfavorable: Tighten stop, consider partial exit
Manual Exit Conditions:
• Stop loss hit: Take loss, log for validation (system expects some losses)
• Profit target hit: If using fixed targets (2-3R typical)
• Major news event: Flatten during high-impact news (system can't predict these)
Warning Signs (Exit Criteria):
🚨 Stop Trading If:
1. All regimes show negative Sharpe for 4+ weeks (market structure changed)
2. Your results >20% worse than shadow portfolios (execution problem)
3. Parameters hitting extremes (thresholds >85 or <35 across all regimes)
4. Memory agreement <30% for extended periods (unprecedented conditions)
5. Account drawdown >20% (risk management failure, system or otherwise)
⚠️ Reduce Size If:
1. Win rate drops 10%+ from peak (temporary regime shift)
2. Selected strategy underperforming another by >30% (selection lag)
3. Consecutive losses >5 (variance or problem, reduce until clarity)
4. Major market regime change (Fed policy shift, war, etc. - let system re-adapt)
PART 15: THEORETICAL IMPLICATIONS & LIMITATIONS
WHAT THIS SYSTEM REPRESENTS:
Contextual Bandits:
The regime-specific strategy selection implements a contextual multi-armed bandit problem. Each strategy is an "arm," each regime is a "context," and we select arms to maximize expected reward given context. This is reinforcement learning applied to trading.
Experience Replay:
The dual-memory architecture mirrors DeepMind's DQN breakthrough. STM = recent experience buffer; LTM = validated experience replay. This prevents catastrophic forgetting while enabling rapid adaptation—a key challenge in neural network training.
Meta-Learning:
The system learns how to learn. Parameter adaptation adjusts the system's own sensitivity and selectivity based on outcomes. This is "learning to learn"—optimizing the optimization process itself.
Non-Stationary Optimization:
Traditional backtesting assumes stationarity (past patterns persist). This system assumes non-stationarity and continuously adapts. The goal isn't finding "the best parameters" but tracking the moving optimum.
Regime-Conditional Policies:
Rather than a single strategy for all conditions, this implements regime-specific policies. This is contextual decision-making—environment state determines action selection.
FINAL WISDOM:
"The market is a complex adaptive system. To trade it successfully, one must also adapt. This indicator provides the framework—memory, learning, regime awareness—but wisdom comes from understanding when to trade, when to stand aside, and when to defer to conditions the system hasn't yet learned. The edge isn't in the algorithm alone; it's in the partnership between mathematical rigor and human judgment."
— Inspired by the intersection of Einstein's relativity, Kahneman's behavioral economics, and decades of quantitative trading research
Taking you to school. — Dskyz, Trade with insight. Trade with anticipation.
Volatility Signal-to-Noise Ratio🙏🏻 this is VSNR: the most effective and simple volatility regime detector & automatic volatility threshold scaler that somehow no1 ever talks about.
This is simply an inverse of the coefficient of variation of absolute returns, but properly constructed taking into account temporal information, and made online via recursive math with algocomplexity O(1) both in expanding and moving windows modes.
How do the available alternatives differ (while some’re just worse)?
Mainstream quant stat tests like Durbin-Watson, Dickey-Fuller etc: default implementations are ALL not time aware. They measure different kinds of regime, which is less (if at all) relevant for actual trading context. Mix of different math, high algocomplexity.
The closest one is MMI by financialhacker, but his approach is also not time aware, and has a higher algocomplexity anyways. Best alternative to mine, but pls modify it to use a time-weighted median.
Fractal dimension & its derivatives by John Ehlers: again not time aware, very low info gain, relies on bar sizes (high and lows), which don’t always exist unlike changes between datapoints. But it’s a geometric tool in essence, so this is fundamental. Let it watch your back if you already use it.
Hurst exponent: much higher algocomplexity, mix of parametric and non-parametric math inside. An invention, not a math entity. Again, not time aware. Also measures different kinds of regime.
How to set it up:
Given my other tools, I choose length so that it will match the amount of data that your trading method or study uses multiplied by ~ 4-5. E.g if you use some kind of bands to trade volatility and you calculate them over moving window 64, put VSNR on 256.
However it depends mathematically on many things, so for your methods you may instead need multipliers of 1 or ~ 16.
Additionally if you wanna use all data to estimate SNR, put 0 into length input.
How to use for regime detection:
First we define:
MR bias: mean reversion bias meaning volatility shorts would work better, fading levels would work better
Momo bias: momentum bias meaning volatility longs would work better, trading breakouts of levels would work better.
The study plots 3 horizontal thresholds for VSNR, just check its location:
Above upper level: significant Momo bias
Above 1 : Momo bias
Below 1 : MR bias
Below lower level: significant MR bias
Take a look at the screenshots, 2 completely different volatility regimes are spotted by VSNR, while an ADF does not show different regime:
^^ CBOT:ZN1!
^^ INDEX:BTCUSD
How to use as automatic volatility threshold scaler
Copy the code from the script, and use VSNR as a multiplier for your volatility threshold.
E.g you use a regression channel and fade/push upper and lower thresholds which are RMSEs multiples. Inside the code, multiply RMSE by VSNR, now you’re adaptive.
^^ The same logic as when MM bots widen spreads with vola goes wild.
How it works:
Returns follow Laplace distro -> logically abs returns follow exponential distro , cuz laplace = double exponential.
Exponential distro has a natural coefficient of variation = 1 -> signal to noise ratio defined as mean/stdev = 1 as well. The same can be said for Student t distro with parameter v = 4. So 1 is our main threshold.
We can add additional thresholds by discovering SNRs of Student t with v = 3 and v = 5 (+- 1 from baseline v = 4). These have lighter & heavier tails each favoring mean reversion or momentum more. I computed the SNR values you see in the code with mpmath python module, with precision 256 decimals, so you can trust it I put it on my momma.
Then I use exponential smoothing with properly defined alphas (one matches cumulative WMA and another minimizes error with WMA in moving window mode) to estimate SNR of abs returns.
…
Lightweight huh?
∞
Mark Minervini SEPA Methodology Trading ToolCore Purpose
Visual toolkit reflecting Mark Minervini’s SEPA trading principles.
Helps identify trend strength, quality consolidations, and avoid overextended entries.
Moving Average Framework
Plots key EMAs/SMAs (5, 10, 20, 50, 150, 200).
Shows clean trend alignment and price respect of key levels.
Trend Template Highlight
Shades area between SMA 150 & SMA 200 when all Minervini Trend Template conditions are met:
Price above 150 & 200 SMA
SMA 150 > SMA 200
Rising 200 SMA
Price above SMA 50
Price 30% above 52-week low
Price within 25% of 52-week high
Daily Extended Detector
Uses ATR to warn when price is too far above the 10-day EMA.
Shaded zone indicates high-risk, overextended conditions.
Consolidation Tools
Weekly Tight Closes Detector: flags 3-week volatility contraction zones.
Inside Day Detector: marks inside bars and emphasizes back-to-back inside days.
Swing-Structure Markers
Automatically labels pivot highs and lows.
Optional %-change between pivot points for trend-rhythm analysis.
Overall Function
A focused, rules-based visual assistant designed to keep charts aligned with Minervini’s strict trend, risk, and consolidation standards.
Ultra Hassas SuperTrend v6 – HEIKEN + 2x + ALARMUltra hassas trend takibi ile dip ve tepelerden gelen sinyallerle hitli bir sekilde kar edilebilir.
Z-Score Regime DetectorThe Z-Score Regime Detector is a statistical market regime indicator that helps identify bullish and bearish market conditions based on normalized momentum of three core metrics:
- Price (Close)
- Volume
- Market Capitalization (via CRYPTOCAP:TOTAL)
Each metric is standardized using the Z-score over a user-defined period, allowing comparison of relative extremes across time. This removes raw value biases and reveals underlying momentum structure.
📊 How it Works
- Z-Score: Measures how far a current value deviates from its average in terms of standard deviations.
- A Bullish Regime is identified when both price and market cap Z-scores are above the volume Z-score.
- A Bearish Regime occurs when price and market cap Z-scores fall below volume Z-score.
Bias Signal:
- Bullish Bias = Price Z-score > Market Cap Z-score
- Bearish Bias = Market Cap Z-score > Price Z-score
This provides a statistically consistent framework to assess whether the market is flowing with strength or stress.
✅ Why This Might Be Effective
- Normalizing the data via Z-scores allows comparison of diverse metrics on a common scale.
- Using market cap offers broader insight than price alone, especially for crypto.
- Volume as a reference threshold helps identify accumulation/distribution regimes.
- Simple regime logic makes it suitable for trend confirmation, filtering, or position biasing in systems.
⚠️ Disclaimer
This script is for educational purposes only and should not be considered financial advice. Always perform your own research and risk management. Past performance is not indicative of future results. Use at your own discretion.
Average True Range % infoATR% is a modified version of the classic Average True Range indicator that displays price volatility as a percentage of the instrument's value, rather than in absolute values. This allows you to easily compare the volatility of different assets (e.g., Bitcoin vs Tesla stock) regardless of their price.
Main Features
1. ATR% Chart
The red line shows the average volatility from the last N candles (default 14), expressed as a percentage. For example:
ATR% = 2.5% means that the average daily move is approximately 2.5% of the asset's value
Higher values = greater volatility (higher profit potential, but also greater risk)
Lower values = lower volatility (calmer market)
2. Volatility Trend Analysis
The indicator automatically detects whether volatility is rising, falling, or stable:
Up arrow (↑) - volatility is rising (price becomes more "nervous")
Down arrow (↓) - volatility is falling (market is calming down)
Horizontal arrow (⮆) - volatility is stable (within ±3% of the moving average)
3. Information Table
In the upper right corner of the chart you will see Current ATR% value and Trend arrow with color coding:
- Green = rising volatility
- Red = falling volatility
- Gray = stable volatility
Parameters to Configure
Indicator Length (default: 14) - How many candles back to include in calculations:
Lower values (5-10): more sensitive to sudden changes, reacts faster
Higher values (20-30): more smoothed, shows long-term volatility picture
Trend Length (default: 10) - Period to analyze whether volatility is rising/falling:
Lower values: faster trend change signals
Higher values: more reliable, but slower signals
Sample Interpretations
ATR% Volatility Asset Type/Situation
< 1% Very low Stable blue-chip stocks, calm market
1-3% Low-medium Typical stocks, normal conditions
3-5% Medium-high Volatile stocks, cryptocurrencies at rest
5-10% High Cryptocurrencies, penny stocks
> 10% Extremely high Market panic, crash, pump & dump
ATH/ATL/DaysThis indicator displays the All-Time High (ATH) and All-Time Low (ATL) — or more precisely, the highest and lowest price within the last N days. It works on any timeframe and uses only local chart data (no security() calls), ensuring stable and accurate results.
It plots horizontal lines for both the ATH and ATL and includes a clean, compact table showing:
Date of the extreme
Days since it occurred
Price
% distance from current price
$ distance from current price
A reliable tool for identifying local extremes, spotting market structure shifts, and tracking short-term price ranges.
SCOB Pattern with ERC & AlertsSingle Candle Block (SC0B) consists of a single candle appearing at a significant price level, indicating a confirmed reversal in price direction from that particular area of interest.
SCOB is primarily used to confirm and execute trades.
Using a single candle block to enter a trade minimizes risk and maximizes reward.
Single bullish candle block?
1st candle closes at bullish point of interest with a short or long wick.
2nd candle sweeps the low of previous(1st) candle and closes above the low of previous candle.
3rd candle closes above the high of 2nd candle.
How to trade with Scob bullish.
To Trade using Bullish SCOB you have to wait for price to come down and test the single candle order block.
When price tests the SCOB you can directly execute a buy trade or for a precise entry you can wait for a market structure shift in lower time frame.
Scob discount is the opposite of price increase.
This strategy should only be used when price "sweeps through key lever, liquidity, imbalance, poi htf areas.
This indicator will add a filter to help you reduce signal noise.
Use the "Use engulfing candle to test" function to filter the 3rd candle.
Only search for Scob if the 3rd candle is an Engulfing candle.
The logic for finding Engulfing candles can be changed based on the "% maximum wick length" option. The default is that the candle wick is 25% of the total candle wick length.
You can also use the alert function when Scob appears
With Smart money concept, no strategy is perfect in trading, so you should not risk too much of your capital on this strategy.
To be safer, always remember to use stop loss for every trade.
QPulseQPulse - A Volume-Normalized RSI Indicator
WHAT THIS IS
QPulse Pro is an RSI-based momentum indicator that solves a major problem most traders face: false signals during low volume periods. You know those overnight spikes or pre-market moves that look extreme but mean nothing? This indicator dampens those signals by normalizing them against actual volume participation.
At its core, it combines volume-weighted RSI with ATR-adaptive RSI in a hybrid calculation that automatically adjusts based on market conditions. The result is an RSI that responds more intelligently to what the market is actually doing, not just price movement alone.
HOW IT WORKS
The indicator calculates RSI in three ways and blends them based on current market regime. When volume is high and confirming price action, it leans into volume-weighted calculations. When volatility is spiking, it adapts the lookback period using ATR. The hybrid mode automatically switches between these approaches depending on what matters most in the current market environment.
The volume normalization is the key differentiator. Every signal gets scaled by volume participation. If volume is at 30 percent of normal, the indicator dampens that signal strength accordingly. Statistically insignificant moves get filtered out so you are not chasing noise.
The heatmap shows historical RSI intensity over time, making it easy to spot when momentum is building or fading across multiple bars rather than just looking at the current reading.
HOW TO USE IT
The indicator displays as a histogram oscillating around a zero line. Positive values indicate bullish momentum, negative values indicate bearish momentum. The dynamic overbought and oversold zones adjust based on volatility and trend strength, so they are not just static 70 and 30 levels that get ignored in strong trends.
For entries, watch for the histogram crossing the zero line with confirmation from volume participation. If the indicator shows extreme readings but volume participation is weak, treat it skeptically. The divergence markers will automatically appear when price makes new highs or lows but RSI does not confirm.
The quant features add additional context. Intermarket divergence alerts you when your asset is behaving differently from correlated markets. CVD proxy tracks cumulative buying versus selling pressure. Volatility of volatility helps predict when breakouts are more likely.
PROS
Honest assessment of what this does well. The volume normalization actually works and eliminates a ton of false signals during illiquid periods. I have tested this across futures, stocks, and crypto and the filtering is consistent.
The hybrid calculation is genuinely adaptive. It does not just use a fixed formula. The indicator monitors market regime in real-time and shifts weighting between volume and volatility factors.
This means it performs reasonably well in both choppy ranges and strong trends.
The divergence detection is more reliable than standard RSI divergences because it is working with volume-adjusted data. You get fewer fake divergence signals.
All the calculations are optimized and cached where possible so it does not lag your charts. The code runs efficiently even with all features enabled.
Multiple timeframe thinking is built in through the advanced ATR calculations which look at efficiency and volatility across different periods.
CONS
Let me be straight about the limitations. This is still an RSI indicator. It is not magic. In extremely strong trending markets, it will stay pegged at extremes just like any momentum oscillator. The dynamic levels help but they do not solve the fundamental issue that RSI is not a great tool in runaway trends.
The volume normalization works great for filtering out noise but it also means you might miss legitimate moves that happen on lower volume. Early trend changes often start quietly before volume kicks in. This indicator will be slower to signal those.
The quant features like intermarket divergence and VoV are sophisticated but they add complexity. If you do not understand what they are measuring, you will probably ignore them or misuse them. They are not magic bullets.
The indicator has a lot of settings. You can tune it for your specific markets and timeframes but that means you need to put in the work to understand what each parameter does. Default settings are reasonable but not optimized for every use case.
It works best on liquid instruments with consistent volume patterns. If you trade low volume altcoins or illiquid options, the volume normalization might not have enough data to work properly.
BEST USE CASES
Futures traders will get the most out of this. The volume normalization shines during overnight sessions and the intermarket divergence features are built specifically for ES, NQ, and RTY traders.
Day traders and scalpers benefit from the real-time volume filtering. You can trust the signals more during regular trading hours.
Swing traders can use the divergence detection and volatility signature features to time entries and exits around multi-day setups.
It works across timeframes but I would say 5-minute to 1-hour charts are the sweet spot. On very low timeframes the volume data gets too noisy. On daily charts you lose some of the intraday volume context.
WHAT YOU SHOULD KNOW
This is not a standalone system. It is a tool that gives you better information about momentum and volume relationship. You still need price action context, support and resistance, and your own trading plan.
The alerts are extensive. There are alerts for overbought, oversold, zero line crosses, divergences, volume spikes, volatility events, and quant signals. You will want to be selective about which ones you enable or you will get alert fatigue.
The indicator performs best when you understand what type of market you are in. Ranging, trending, or volatile. The hybrid mode tries to auto-detect this but you should still be aware of the bigger picture context.
Volume normalization means the indicator will be quieter during low participation periods. This is by design. If you prefer indicators that always give signals regardless of volume, this is not for you.
FINAL THOUGHTS
This is a serious tool for serious traders who understand that volume and price need to be analyzed together. It is not going to make trading easy but it will give you better quality information to make decisions with. The learning curve is real but if you put in the time to understand how it works, it genuinely adds value.
Use it as part of a complete trading approach. Combine it with your price action analysis, risk management, and market structure awareness. The indicator will tell you when momentum is legitimate and when it is just noise. What you do with that information is still up to you.
Đại Ka 3 ATR BandsĐại Ka 3 ATR Bands – The ultimate single-slot indicator that replaces three separate ATR plots.
Designed specifically for ICT/SMC traders in 2025:
• Light red band (±0.5 ATR) → fake moves, Judas Swing, Turtle Soup zone
• Gray band (±1.0 ATR) → normal price action
• Light green band (±2.0 ATR) → real displacement zone → Silver Bullet, SFT, high-probability entries
How to use:
– Price stuck inside red band → expect reversal/fakeout
– Price breaks and closes outside green band + volume spike → enter aggressively in that direction (85%+ win-rate inside Killzones)
Default ATR(14), subtle fills for instant visual filtering of real vs fake moves.
Perfect companion for Order Blocks, FVG, Breaker Blocks and NY/London Killzones.
Free forever – coded with love by Đại Ka & Vietnamese ICT crew.
ATR multiple from High & LowA simple numerical indicator measuring ATR multiple from recent 252 days high and low.
ATR multiples from high (and low) are used as a base in many systematic trading and trend following systems. As an example many systems buy after a 2.5–4 ATR multiple pullback in a strong stock if the regime allows it. This would then be paired with an entry tactic, for example buy as it recaptures the a pivot within the upper range, a MA or breaks out again after this mid term pullback/shakeout.
This indicator uses a function which captures the recent high and low no matter if we have 252 bars or not, which is not how standard high/low works in Tradingview. This means it also works with recent IPO:s.
I prefer to overlay the indicator in one of the lower panes, for example the volume pane and then right click on the indicator and select Pin to scale > No scale (fullscreen).
Prev Day/Week Breakout Signals (15m, 1st 15 min BO)- Dr VinayPrev Day/Week Breakout Signals (15m, First Candle Only)- For taking break out entries
TSO PRO – Trend & Momentum Unified Engine v2**TSO PRO exposes hidden reversal energy before the chart moves.
Traders call it the earliest structural signal they’ve ever used.**
🔥 TSO PRO — High-Resolution Trend & Momentum Engine
TSO PRO detects structural reversals before price reacts.
If you trade turning points, momentum, or compression/release patterns —
this is your edge.
⭐ What Makes TSO PRO Different
Most oscillators average price → signals arrive late.
TSO PRO interprets structural behavior inside price, giving:
early reversal detection
momentum compression/expansion mapping
unified trend + momentum transitions
deeper resolution than MACD/RSI/Stoch
TSO PRO reveals the actual internal pressure of the market — not just the lagging result.
🚀 What You Get in TSO PRO
TSO PRO includes the full multi-layer signal engine:
TSO Line+ – refined directional feedback
TSO Flow+ – structural flow dynamics
TSO Pulse+ – compression & release sequences
TSO Drive – active directional pressure
TSO Extremes – turning-zone probability
Automation Layer – full webhook alert support
High-resolution unified engine (PRO-only)
This system is built for traders who need earlier transitions and automation-ready output.
🆚 TSO Lite vs TSO PRO (Quick Comparison)
Feature Lite PRO
Core Engine ✔ Basic 3 layers ✔ Full multi-layer engine
Structural Flow ✔ Basic ✔ High-resolution
Turning-Zone Detection ✘ ✔ TSO Extremes
Directional Pressure ✘ ✔ TSO Drive
Automation (Webhook) ✘ ✔ Yes
Best For Beginners Advanced traders
TSO Lite is great for learning the structure —
TSO PRO is for actual decision-making & automation.
🔗 Access TSO PRO
TSO PRO is available via subscription:
Monthly: tradesmith6.gumroad.com/l/tso-pro
Yearly (Best Value): tradesmith6.gumroad.com/l/tso-pro-yearly
Subscribers receive all updates, improvements, and priority support.
📌 License
TSO PRO is invite-only and protected.
Redistribution, resale, or reverse-engineering is prohibited.
⚠️ Disclaimer
Trading carries risk. TSO PRO does not guarantee profit.
🇰🇷 CTR·전환율 최적화 버전 (한국어)
🔥 TSO PRO — 구조 기반 추세·모멘텀 고해상도 엔진
TSO PRO는 가격이 움직이기 전에 구조적 전환을 감지합니다.
전환구간, 모멘텀 변화, 압축/이완 패턴을 거래한다면
이 지표는 분명한 우위를 제공합니다.
⭐ TSO PRO가 특별한 이유
일반적인 오실레이터는 대부분 평균 기반 → 신호가 늦게 나옵니다.
TSO PRO는 가격 내부 구조 변화를 해석하여:
조기 반전 감지
모멘텀 압축·폭발 구간 식별
추세 + 모멘텀 전환 통합 뷰
MACD/RSI/스토캐스틱보다 더 높은 해상도
즉, 단순한 “결과”가 아니라 시장 내부 압력 그 자체를 보여줍니다.
🚀 TSO PRO 제공 기능 (PRO 전용)
TSO Line+ – 세밀한 방향 반응
TSO Flow+ – 고해상도 구조 흐름
TSO Pulse+ – 압축/이완 패턴
TSO Drive – 방향성 압력
TSO Extremes – 전환구간 확률
Webhook 자동매매 지원
멀티-레이어 통합 엔진 전체 구성
고급 트레이더에게 필요한 초기 전환 감지 + 자동화 대응을 제공합니다.
🆚 Lite vs PRO (요약 비교)
기능 Lite PRO
핵심 엔진 ✔ 기본 3레이어 ✔ 전체 멀티레이어
구조 흐름 ✔ 기본 ✔ 고해상도
전환구간 감지 ✘ ✔ Extremes
방향 압력 ✘ ✔ Drive
자동매매(Webhook) ✘ ✔ 지원
적합한 사용자 초보자 고급 트레이더
TSO Lite는 구조 이해용
TSO PRO는 실제 매매·자동화용입니다.
🔗 TSO PRO 구독
월간: tradesmith6.gumroad.com/l/tso-pro
연간(가성비): tradesmith6.gumroad.com/l/tso-pro-yearly
구독 시 지속 업데이트 + 우선 지원 제공.
📌 라이선스
TSO PRO는 유료 초대형 지표입니다.
재배포·재판매·역설계는 금지됩니다.
⚠️ 면책
본 지표는 수익을 보장하지 않으며 모든 매매 책임은 사용자에게 있습니다.
Reward-Risk Visual Zones (RRZ)Reward–Risk Visual Zones (RRZ) – Structure-Driven Risk Mapping Engine
Most indicators tell you when to trade.
RRZ is designed to answer a different question:
“Is this setup worth the risk before I take the trade?”
RRZ is not a mashup of unrelated indicators.
It is a 3-stage risk engine that builds a complete trade “story” around each swing:
1. Market structure pivot → defines the trade idea
2. ATR volatility unit → defines the risk size
3. Projected reward → compares reward vs that risk and filters bad setups
The output is a visual trade map : entry, target, stop, and R:R, printed directly on chart at each qualified swing.
1. Market Structure Layer – Where trades are anchored
RRZ doesn’t fire signals from MA/RSI crossovers.
It begins with s wing structure :
• Uses ta.pivotlow() and ta.pivothigh() with user-controlled left/right sensitivity:
• Pivot Sensitivity (L) controls how wide a swing must be to qualify as a structural pivot.
• Pivot Confirmation (R) controls how fast/slow a pivot is confirmed (1 = almost real-time, larger values = safer but later).
• A confirmed swing low becomes the candidate anchor for a long R:R zone.
• A confirmed swing high becomes the candidate anchor for a short R:R zone.
Each pivot is stored with its exact price and bar index , so the rest of the logic is always anchored to a real swing instead of a random candle.
2. Volatility Layer – Turning ATR into a “risk unit”
Once a pivot is confirmed, RRZ measures local volatility using ATR:
• ATR length is configurable via ATR Length.
• ATR is not used as a trailing stop or overlay.
In RRZ it plays one specific role:
“One ATR = one unit of structural risk from this swing.”
From that:
Long scenario:
• Entry = pivot low
• Stop = Entry − 1 × ATR
Short scenario:
• Entry = pivot high
• Stop = Entry + 1 × ATR
This standardizes risk across different volatility regimes:
a 2R move in a quiet market and a 2R move in a volatile market are both “2 × ATR” from the structure.
3. Reward Projection Layer – Evaluating if the trade is worth it
RRZ then estimates how far price can realistically travel away from the pivot, given recent behaviour:
For longs (from swing low):
• Projects reward using the highest high in a rolling lookahead window (e.g., last 20 bars).
• Reward = HighestHigh – PivotLow
• Risk = ATR
• R:R = Reward / ATR
For shorts (from swing high):
• Projects reward using the lowest low in the window.
• Reward = PivotHigh – LowestLow
• Risk = ATR
• R:R = Reward / ATR
The user can set a minimum R:R filter via Min R:R Threshold.
• If R:R ≥ threshold → zone is tagged as “ GOOD R:R ” and highlighted in green (long) or red (short).
• If R:R < threshold → optionally still shown as “ LOW R:R ” in orange (so you can visually see “tempting but not worth it” trades).
This transforms a raw pivot + ATR into a quantified decision:
“From this swing, volatility says my stop is 1× ATR. Do I even have 2× ATR of room in my favour?”
4. Execution Map – How components work together on chart
Instead of just printing numbers, RRZ builds a full execution template for every qualifying swing:
• Entry line at the pivot price
• Target line at the reward projection price
• Stop line at pivot ± ATR
• A structured label including:
• LONG / SHORT direction
• “GOOD R:R” or “LOW R:R”
• Numerical R:R (e.g., 2.3 : 1)
• Entry, Target, Stop, current Close
A Cooldown Bars parameter prevents overlapping spam:
• After an R:R zone is created (long or short), the script waits X bars before considering another zone in the same direction.
• This keeps charts readable and focuses attention on the most recent high-quality swings.
RRZ also includes line style controls (solid/dashed/dotted) and separate colours for entry, target, and stop, so traders can quickly read the chart even without opening settings.
5. How to Use RRZ in a Trading Plan
RRZ is not a magic “buy/sell” button.
It is designed to sit on top of your existing strategy as a risk filter.
Typical workflows:
SMC / price-action traders:
• Use your own logic for BOS/CHoCH, order blocks, or liquidity sweeps.
• Use RRZ only where structure aligns and the zone shows GOOD R:R ≥ 2.0.
• Avoid setups where RRZ marks LOW R:R even if the pattern looks good.
System traders / swing traders:
• Use RRZ to standardize risk across assets and timeframes.
• Filter out trades where potential reward does not justify the stop, based on current ATR.
Beginners:
• Learn to stop taking trades where the target is too close and the stop is too wide.
• Visually understand how structural swings and volatility interact.
RRZ works across Forex, Crypto, Indices, and Stocks on intraday and higher timeframes.
Once a pivot is confirmed and a zone is plotted, its lines and labels do not repaint.
⸻
6. Why this invite-only script is more than a basic mashup
RRZ does use classic building blocks (pivots, ATR), but the value is in the way they are stitched into a single risk framework:
• It doesn’t run multiple separate indicators on chart.
Everything is computed inside one engine with a single purpose: structure-based R:R evaluation.
• ATR is not just “slapped on” as a stop line; it is treated as the core risk unit in the R:R calculation pipeline.
• Every zone is a coherent trade idea: from swing, to risk unit, to projected reward, to filtered label, to execution lines.
• The script is designed specifically to help traders do the one thing that almost no free indicator prioritizes:
“Say NO to low R:R trades automatically.”
This is what distinguishes RRZ from generic ATR-stop scripts or simple pivot overlays.
⸻
Important
• No performance promises.
• No marketing claims (“guaranteed”, “high win rate”, etc.).
• Purely a risk mapping & visualization tool.
MTRA Pro+ ScreenerMTRA Pro+ Screener an analysis tool that provides traders with critical market structure information on up to 10 instruments simultaneously. This indicator consolidates momentum direction, trend analysis, range relationships, and volatility metrics into a single dashboard.
## Key Features
- Customizable display with adjustable positioning, colors, and sizing
**Momentum & Trend Tracking**
- Real-time momentum direction via 5-period SMA slope analysis
- Short-term trend direction using 10-period SMA slope analysis
- Color-coded visual representation for quick interpretation
**Range Relationship Analysis**
- Current bar analysis relative to previous period (Inside, Outside, 2Up, 2Dn)
- Three-period historical view of recent price action patterns
- Immediate identification of breakout and consolidation scenarios
**ATR-Based Volatility Analysis**
- Real-time ATR percentage calculations showing current range vs. average
- Visual distinction between normal (<100% ATR) and extended (>100% ATR) conditions
- Identification of potential exhaustion zones for risk management
**Intraday ATR Levels**
- Dynamic support/resistance levels based on current timeframe ATR
- Real-time upper and lower boundaries for precise entries/exits
- Customizable line styles integrated with price scale
## Practical Applications
- **Context Assessment**: Quickly gauge market conditions across multiple intraday timeframes
- **Exhaustion Detection**: Identify overextended moves when ATR exceeds 100%
- **Confluence Analysis**: Spot potential setups when timeframes align
- **Risk Management**: Some traders will use ATR levels for dynamic stops and position sizing
- **Breakout Confirmation**: Distinguish false breakouts from genuine momentum shifts
## Configuration Options
- Full dashboard positioning and color customization
- Individual timeframe toggles
- Adjustable ATR periods and sensitivity thresholds
- Multiple line styles for level visualization
Quad VixThis indicator shows the VIX at 4 different time periods in one horizontal panel.
1. VIX9D = 9 day VIX (green).
2. VIX = 30 day VIX (red).
3. VIX3M = 3 month VIX (purple).
4. VIX6M = 6 month VIX (blue).
Feedback genuinely welcomed.
MTRA Intraday Pro+MTRA Pro Intraday is a multi-timeframe analysis tool that provides traders with critical market structure information across five intraday timeframes: 5m, 15m, 30m, 1h, and 4h. This indicator consolidates momentum direction, trend analysis, range relationships, and volatility metrics into a single dashboard.
***Dashboard Timeframes that are low than chart timeframes can be inaccurate - Because of this always focus attention on the details on higher timeframes for accurate data****
## Key Features
**Multi-Timeframe Analysis**
- Simultaneous analysis across 5m, 15m, 30m, 1h, and 4h timeframes
- Customizable display with adjustable positioning, colors, and sizing
**Momentum & Trend Tracking**
- Real-time momentum direction via 5-period SMA slope analysis
- Short-term trend direction using 10-period SMA slope analysis
- Color-coded visual representation for quick interpretation
**Range Relationship Analysis**
- Current bar analysis relative to previous period (Inside, Outside, 2Up, 2Dn)
- Three-period historical view of recent price action patterns
- Immediate identification of breakout and consolidation scenarios
**ATR-Based Volatility Analysis**
- Real-time ATR percentage calculations showing current range vs. average
- Visual distinction between normal (<100% ATR) and extended (>100% ATR) conditions
- Identification of potential exhaustion zones for risk management
**Intraday ATR Levels**
- Dynamic support/resistance levels based on current timeframe ATR
- Real-time upper and lower boundaries for precise entries/exits
- Customizable line styles integrated with price scale
## Practical Applications
- **Context Assessment**: Quickly gauge market conditions across multiple intraday timeframes
- **Exhaustion Detection**: Identify overextended moves when ATR exceeds 100%
- **Confluence Analysis**: Spot high-probability setups when timeframes align
- **Risk Management**: Use ATR levels for dynamic stops and position sizing
- **Breakout Confirmation**: Distinguish false breakouts from genuine momentum shifts
## Configuration Options
- Full dashboard positioning and color customization
- Individual timeframe toggles
- Adjustable ATR periods and sensitivity thresholds
- Multiple line styles for level visualization
## Who Benefits
- **Scalpers**: 5m/15m alignment for quick entries
- **Day Traders**: Multi-timeframe confluence for swing entries within the day
- **Intraday Swing Traders**: 1h/4h context for position holds
MTRA Pro Intraday transforms complex multi-timeframe analysis into clear, actionable market structure data without switching charts. This tool enhances decision-making by providing objective context across all relevant intraday timeframes in one view.
Smart Money Concepts [Dau_tu_hieu_goc]Credits to LuxAlgo for the SMC Parts.
Edited by Dau_Tu_Hieu_Goc
Pele CandlesPele Candles Indicator
Named after Pele, the Hawaiian goddess of volcanoes, this indicator identifies "explosive" candles with unusually large ranges that exceed a customizable ATR (Average True Range) threshold. These volcanic-like price movements often signal significant market activity where liquidity may have been swept from one side.
Pele candles appear as colored bars (blue for bullish, purple for bearish) when candle ranges surpass the ATR multiplier. While a single Pele candle doesn't guarantee direction, consecutive Pele candles in opposite directions can indicate potential trend reversals - much like volcanic eruptions that reshape the landscape.
The indicator helps traders spot moments of intense market activity and potential turning points, but should be used alongside other analysis tools for confirmation.
Features:
Customizable ATR period and multiplier
Visual highlighting of explosive price moves
Alert notifications for significant candles
No repainting - signals appear in real-time
******Make sure you go to visual order settings and bring to front******
