Volume Matrix Pro [ChartNation]Volume Matrix Pro is a comprehensive volume profile indicator that combines delta-colored volume distribution analysis with adaptive pivot detection and automated volume node identification. The indicator visualizes where institutional volume accumulated at specific price levels, providing traders with precise entry zones backed by actual trading data.
KEY FEATURES:
Delta-Colored Volume Profile: Displays volume distribution across price bins with automatic delta coloring - green bins show buyer dominance, red bins show seller control at each price level
High Volume Nodes (HVN) Detection: Automatically identifies and marks price levels with ≥80% of POC volume using yellow diamond markers - these act as magnetic support/resistance zones where institutions built positions
Low Volume Nodes (LVN) Detection: Marks thin volume areas with gray diamond markers - zones where price moves quickly with minimal friction, ideal for breakout targets
Adaptive Smart Pivots: ATR-based pivot detection that automatically adjusts length based on market volatility - catches more swings in low volatility, filters to major reversals in high volatility
Point of Control (POC) Line: Identifies the price level with maximum traded volume - the market's center of gravity. Line colors by delta: green when buyers dominated, red when sellers controlled the level
Value Area Lines: Dotted lines marking the 70% value area (configurable 50-98%) with delta-based coloring showing cumulative buyer/seller pressure within the range
Circle Pivot Markers: Clean visual markers at confirmed pivot points with translucent horizontal lines extending to current bar
Extend-Until-Touch: Pivot lines automatically retract when price touches them, keeping charts clean and showing active levels only
Dual Profile Modes: Left-side profile (default) or right-pinned bars ahead of price with fully customizable width and padding
Volume-Filtered Pivots: Only displays pivots with significant volume backing (≥20% of POC by default) - institutional turning points, not noise
HOW IT WORKS:
The indicator divides the lookback range (default 200 bars) into volume bins (default 50) and calculates total volume and delta (buying vs selling pressure) at each price level. Each bin is colored green if buyers dominated (close > open majority) or red if sellers controlled (close < open majority).
High Volume Nodes mark price levels where the most trading occurred - these become magnetic support/resistance zones. The Point of Control identifies the single price with maximum volume, acting as the market's gravitational center.
Smart Pivots use ATR to adapt to changing volatility, then filter against the volume profile. Only pivots with substantial volume backing are displayed, ensuring you see institutional turning points, not random noise.
RECOMMENDED SETTINGS:
Scalping (1-5 min): 100 lookback bars, 40 bins, 5-7 pivot length
Day Trading (15 min - 1 hour): 200 lookback bars, 50 bins, 10 pivot length (default)
Swing Trading (4 hour - Daily): 300-500 lookback bars, 60 bins, 15-20 pivot length
USAGE TIPS:
Enter long when price touches green HVN zones with adaptive pivot confirmation
Enter short when price reaches red HVN zones with pivot confirmation
Use POC as first target when entering below it, or as support backup when entering above
Watch for LVN zones as potential breakout acceleration areas
Combine green delta bins + HVN + pivot for highest-probability setups
WHAT MAKES THIS DIFFERENT:
Unlike traditional volume profiles, Volume Matrix Pro colors each bin individually by delta, giving granular insight into buyer/seller control at every price level. The adaptive pivot system adjusts automatically to volatility, while volume-filtering ensures only institutionally-backed turning points are displayed. High/Low Volume Node detection is fully automated with visual markers.
IMPORTANT NOTES:
This is a volume analysis tool - use with trend analysis and risk management
High Volume Nodes show where volume accumulated historically, not future support/resistance guarantees
Adaptive pivots adjust to volatility automatically but can still produce false signals in choppy markets
Best used as confirmation alongside price action, not as a standalone system
Profile recalculates on each bar to reflect current lookback range
Trend Analysis
NeuraEdge Block Trades v1.0NEURAEDGE BLOCK TRADES
═══════════════════════════════════════════════════════════════════════
We are excited to release Block Trades!
WHY THIS INDICATOR EXISTS?
Retail traders face a fundamental challenge: institutions move markets, but their activity is hidden. When smart money accumulates at support or distributes at resistance, retail traders often find themselves on the wrong side of the move.
Understanding where institutions are actively buying or selling is crucial for:
• Validating trade setups with volume confirmation
• Identifying supply and demand zones that actually hold
• Avoiding false breakouts driven by retail sentiment
• Spotting accumulation before major moves up
• Detecting distribution before major moves down
Most volume indicators simply show size without context. Block Trades was created to bridge this gap by detecting abnormally large volume bars and determining their directional bias, giving retail traders insight into institutional activity.
═══════════════════════════════════════════════════════════════════════
WHAT IT DOES:
Block Trades identifies volume spikes that likely represent institutional order flow and classifies them as buying pressure, selling pressure, or contested zones. The indicator then validates these prints against directional flow analysis and groups nearby prints into accumulation or distribution clusters.
This helps you answer critical questions:
• Is this support level being defended by institutions?
• Are smart money players distributing into this rally?
• Is heavy volume confirming my trade or warning against it?
• Where are institutional interest zones forming?
KEY FEATURES:
• Multi-tier volume detection (Large: 2x, Huge: 3x, Massive: 5x average)
• Directional classification with flow validation
• Accumulation/distribution zone detection
• Print clustering for institutional interest areas
• Confluence scoring system (0-10 points)
• Real-time statistics dashboard
• Clean, minimal chart labels
═══════════════════════════════════════════════════════════════════════
HOW IT WORKS:
VOLUME SPIKE DETECTION
The indicator monitors volume against a moving average baseline. When current volume significantly exceeds this average (default thresholds: 2x, 3x, 5x), it flags the bar as a potential institutional print.
DIRECTIONAL CLASSIFICATION
Buy Print: Large volume + closes in top 70% of range
Sell Print: Large volume + closes in bottom 70% of range
Neutral Print: Large volume + mid-range close (absorption/contested)
The close position within the bar's range reveals who won the battle. A bar with massive volume that closes near its high indicates aggressive buying. The same volume closing near the low indicates aggressive selling.
FLOW VALIDATION
Each print is validated against underlying institutional flow calculations. This filters out volume spikes that don't align with directional pressure, significantly reducing false signals. Buy prints require bullish flow, sell prints require bearish flow.
ACCUMULATION & DISTRIBUTION ZONES
When multiple prints occur at similar price levels with consistent direction:
• Repeated buy prints + bullish trend = Accumulation (institutions building positions)
• Repeated sell prints + bearish trend = Distribution (institutions unloading positions)
These zones often become powerful support/resistance levels because institutions have established significant positions there.
PRINT CLUSTERING
The indicator groups nearby prints (within configurable ATR distance) into clusters. When 3 or more prints form a cluster, it marks an institutional interest zone. These clusters frequently act as price magnets and reversal points.
PRINT CLUSTERING
The indicator groups nearby prints (within configurable ATR distance) into clusters. When 3 or more prints form a cluster, it marks an institutional interest zone. These clusters frequently act as price magnets and reversal points.
CONFLUENCE SCORING
Each print receives a confluence score (0-10 points) based on:
• Volume size (Massive: +3, Huge: +2, Large: +1)
• Flow alignment (+2 points, configurable)
• Trend alignment (+1)
• New high/low made (+1)
• Extreme close position (+1)
Prints with 5+ points receive a star marker, indicating ultra-high conviction setups.
═══════════════════════════════════════════════════════════════════════
HOW TRADERS USE IT:
USE CASE 1: TRADE VALIDATION
Your system signals a long entry at support. Check Block Trades:
• Buy prints present at this level? Institutions defending = Take the trade
• Sell prints present? Institutions distributing = Skip or wait
• No prints? Proceed with normal risk management
USE CASE 2: IDENTIFYING EXHAUSTION
Price rallies to resistance with heavy volume:
• Sell prints appear = Distribution, institutions unloading into strength
• Likely reversal coming, consider shorts or exit longs
• Confirmed by multiple sell prints = High conviction reversal setup
USE CASE 3: FINDING SUPPORT/RESISTANCE
Accumulation cluster forms at 450 level:
• Multiple buy prints over several sessions
• Institutions building positions at this price
• 450 becomes high-probability support for future pullbacks
• Use for entries or stop placement
USE CASE 4: BREAKOUT CONFIRMATION
Price breaks above key resistance:
• Buy print on breakout bar = Real institutional participation
• High confluence score (5+) = Ultra-high conviction
• Fake breakout would show sell prints or no prints
USE CASE 5: AVOIDING TRAPS
Price spikes up on huge volume:
• Sell print appears (closes low in range) = Trap
• Institutions selling into retail FOMO
• Avoid chasing, prepare for reversal
═══════════════════════════════════════════════════════════════════════
VISUAL ELEMENTS:
ON-CHART LABELS
Buy Print: Green label below bar showing size (LARGE/HUGE/MASSIVE)
Sell Print: Red label above bar showing size
Contested Print: Orange label at bar high (large volume, mid-range close)
Accumulation: Green "ACCUM" label with diamond symbol
Distribution: Red "DISTRIB" label with diamond symbol
WHAT CONTESTED MEANS:
When a bar has massive volume but closes in the middle of its range (neither top nor bottom 70%), it indicates a battle between buyers and sellers with no clear winner. This often occurs at:
• Major support/resistance levels where institutions are absorbing supply/demand
• Transition zones before a directional move
• Areas of genuine price discovery and uncertainty
Contested prints can signal absorption (institutions quietly building positions) or genuine indecision. Watch for follow-through on the next bar to determine which side won.
LABEL MODIFIERS
∆ checkmark = Flow validated (institutional flow aligns with print)
Star symbol = High confluence (5+ points, ultra-high conviction)
CLUSTER ZONES
Semi-transparent boxes marking areas where multiple prints occurred
Extend to the right to show ongoing institutional interest zones
Color-coded: green for bullish clusters, red for bearish clusters
DASHBOARD (TOP RIGHT)
• Current volume state and ratio
• Institutional flow direction
• Cumulative trend direction
• Recent print count (last 20 bars)
• Active cluster count
• Volume thresholds
STATISTICS (BOTTOM LEFT)
• Total session prints
• Buy/sell percentage split
═══════════════════════════════════════════════════════════════════════
SETTINGS:
PRINT DETECTION
• Volume Lookback Period: 20 bars (for average calculation)
• Large Print Threshold: 2.0x average
• Huge Print Threshold: 3.0x average
• Massive Print Threshold: 5.0x average
• Min Candle Size: 0.3x ATR (filters doji bars)
CLASSIFICATION
• Directional Threshold: 70% (how far in range to qualify as buy/sell)
• Show Neutral Prints: Toggle contested zones
• Require New High/Low: Optional stricter filter
INSTITUTIONAL FLOW
• Enable Flow Confluence: On/Off toggle
• Flow Confluence Weight: 2 points (adjustable 1-5)
CLUSTERING
• Enable Clustering: On/Off
• Cluster Distance: 1.0x ATR (how close prints must be)
• Min Prints for Cluster: 3 prints
• Show Cluster Zones: On/Off
DISPLAY
• Show Print Labels: Toggle all labels
• Show Accumulation/Distribution/Contested Labels: Toggle special labels
• Label Size: Tiny/Small/Normal
• Colors: Customizable buy/sell/neutral colors
FILTERS
• Minimum Volume: 0 (set threshold to ignore low volume bars)
• Session Filter: Avoid first/last 15 minutes (low liquidity)
═══════════════════════════════════════════════════════════════════════
BEST PRACTICES:
DO:
✓ Use as confluence with your primary trading system
✓ Pay attention to accumulation/distribution zones
✓ Look for high confluence prints (5+ stars)
✓ Validate breakouts with print direction
✓ Use cluster zones as future support/resistance
✓ Combine with higher timeframe analysis
✓ Works best on liquid instruments (major pairs, indices, large cap stocks)
DON'T:
✗ Trade prints as standalone buy/sell signals
✗ Ignore the directional classification (context matters)
✗ Use on low-volume instruments (prints less reliable)
✗ Chase every print without confluence confirmation
✗ Trade during low liquidity hours (first/last 15 min)
✗ Expect 100% accuracy (it's a confluence tool, not crystal ball)
OPTIMAL TIMEFRAMES:
• 5-minute to 1-hour charts for intraday trading
• 1-hour to 4-hour charts for swing trading
• Daily charts for position trading
BEST INSTRUMENTS:
• Major forex pairs (EUR/USD, GBP/USD, etc.)
• Index futures (ES, NQ, YM)
• High-volume stocks (SPY, QQQ, TSLA, AAPL, etc.)
• Major cryptocurrencies (BTC, ETH)
═══════════════════════════════════════════════════════════════════════
IMPORTANT DISCLAIMERS
METHODOLOGY DISCLAIMER
This indicator identifies abnormally large volume bars and estimates their directional bias based on price action and flow analysis. It does NOT have access to:
• Actual dark pool transaction data
• Off-exchange Alternative Trading System (ATS) prints
• Level 2 order book data
• Individual trade sizes or timestamps
• Institutional order identification
The prints detected are estimates based on publicly available volume and price data from TradingView. They indicate probable institutional activity patterns but are not confirmed block trades or dark pool executions.
USAGE DISCLAIMER
Block Trades is designed as a CONFLUENCE tool to validate trade setups - not as a standalone trading system. The indicator does not:
• Generate specific entry/exit signals
• Provide stop loss or take profit levels
• Constitute a complete trading strategy
• Guarantee profitable trades
Prints should be interpreted within the context of:
• Your overall trading strategy
• Market structure and trend
• Support/resistance levels
• Risk management rules
• Multiple timeframe analysis
RISK DISCLAIMER
Trading involves substantial risk of loss and is not suitable for every investor. Past performance is not indicative of future results. This indicator is a tool for technical analysis only and does NOT constitute financial advice, investment advice, trading advice, or a recommendation to buy or sell any securities or financial instruments.
You should not make any investment decision without conducting your own research and due diligence. The accuracy, completeness, and timeliness of the information provided by this indicator is not guaranteed. No representation is being made that using this indicator will guarantee profits or prevent losses.
By using this indicator, you acknowledge that you understand and accept all risks associated with trading, and you agree that the developer is not liable for any losses you may incur.
═══════════════════════════════════════════════════════════════════════
ALERTS
Available alert conditions:
• Massive Buy Print
• Massive Sell Print
• Huge Buy Print
• Huge Sell Print
• Accumulation Detected
• Distribution Detected
• High Confluence Buy (5+ points)
• High Confluence Sell (5+ points)
Happy Trading!
Classic Dual Momentum – 12-Month Absolute Momentum - AntonacciThis indicator calculates the 12-month absolute momentum exactly as described in Gary Antonacci’s Dual Momentum framework.
It automatically adjusts the lookback period based on the chart’s timeframe:
Daily chart: 252 bars
Weekly chart: 52 bars
Monthly chart: 12 bars
Other timeframes: Estimated automatically using bar time difference
The script computes the 12-month rate of return and displays it as a color-coded column plot:
Green: Positive 12-month momentum
Red: Negative 12-month momentum
A customizable moving average is included to help visualize longer-term trends in the momentum signal.
How It’s Used (from Dual Momentum theory)
This indicator provides the absolute momentum filter used in classic Dual Momentum strategies:
If the 12-month return of an asset is above the risk-free return → trend is positive
If it is below the risk-free return → trend is negative
This absolute momentum check is a key component of the Global Equities Momentum (GEM) model presented in Gary Antonacci’s book Dual Momentum Investing.
Why This Indicator Exists
It gives traders a clean, accurate way to visualize the 12-month trend strength across any timeframe, without the distortions caused by bar length differences.
Scaling_mastery:Free TrendlinesScaling_mastery Trendlines is a clean, trading-ready smart trendline tool built for the Scaling_mastery community.
It automatically finds swing highs/lows and draws dynamic trendlines or channels that stay locked to price, on any symbol and any timeframe.
🔧 Modes
Trendline type
Wicks – classic trendlines anchored on candle wicks (high/low).
Bodies – trendlines anchored on candle bodies (open/close), great for closing structure.
Channel – 3-line channel:
outer lines form a band around price
middle line runs through the centre of the channel
thickness is adjustable (Small / Medium / Large).
Trend strength
Controls how strong the pivots must be to form a line.
Weak → more lines, reacts faster.
Medium → balanced, good for most pairs.
Strong → only the cleanest swings, higher-probability trendlines.
🎨 Visual controls
Max support / resistance lines – cap how many lines are kept on chart.
Show broken lines – hide broken trendlines or keep them for structure history.
Extend lines – None / Right / Both.
Support / Resistance colors – separate colors for active vs broken.
Channel thickness – Small / Medium / Large (0.5% / 1% / 2% of price).
Channel outer lines – color for channel edges.
Channel middle line – color + style (dotted / dashed / solid).
Broken lines are automatically faded + dotted, so you can instantly see what’s still respected and what’s already been taken out.
🧠 How to use
Add the indicator to any chart.
Start with:
Trendline type: Wicks
Trend strength: Strong
Max lines: 1–2 for both support & resistance
Once you like the behavior, experiment with:
Switching between Wicks / Bodies / Channel
Adjusting Channel thickness and Trend strength
Use the lines as a visual confluence tool with your own strategy:
HTF trend direction
LTF entries / retests
Liquidity grabs around broken lines
This script doesn’t generate entries or risk management – it’s designed to give you clean, reliable structure so you can execute your own edge.
⚠️ Disclaimer
This tool is for educational and visual purposes only and is not financial advice.
Always do your own research and manage risk.
Reversal Candlestick Setups (Doji, Outside, Extreme, Wick)Reversal Candlestick Setups – Doji, Outside, Extreme & Wick
This indicator identifies four high-probability reversal candlestick patterns across all timeframes: Doji Reversals, Outside Reversals, Extreme Reversals, and Wick Reversals. Each setup is based on clearly defined quantitative rules, allowing traders to filter noise and focus on strong reversal signals instead of relying on subjective visual interpretation.
The tool automatically scans every candle, highlights qualifying patterns on the chart, and provides alert options for both bullish and bearish versions of all four setups. This makes it suitable for intraday traders, swing traders, and positional traders seeking early reversal confirmation.
Included Setups
1. Doji Reversal Setup
Identifies candles with extremely small bodies relative to their range, combined with a smaller-than-average bar size. Useful for spotting market indecision before a directional shift.
2. Outside Reversal Setup
Flags candles that engulf the previous candle’s high–low range and exceed the average range by a multiplier. This is designed to capture strong momentum reversals driven by aggressive buying or selling.
3. Extreme Reversal Setup
Highlights large-bodied candles that dominate their overall range and exceed twice the average bar size. These signals aim to catch climactic exhaustion and institutional-level reversals.
4. Wick Reversal Setup
Detects candles with long rejection wicks, small bodies, and closes near an extreme of the range, supported by above-average bar size. Ideal for identifying sharp intrabar rejections.
Key Features
• Automatically detects all four reversal setups
• Works on all timeframes and symbols
• Customizable variables for deeper testing and optimization
• Clear bullish and bearish labels directly on the chart
• Fully integrated alert conditions for real-time notifications
• Suitable for crypto, stocks, indices, forex, and commodities
Who This Indicator Is For
• Traders who want objective, rule-based reversal detection
• Price action traders looking to enhance accuracy
• Systematic traders wanting quantifiable candlestick criteria
• Beginners learning reversal structures with visual guidance
• Professionals integrating reversal patterns into algorithmic or discretionary systems
How to Use
Add the indicator to your chart and enable alerts for the specific setups you want to track (e.g., “Bullish Wick Reversal”). Combine these signals with market structure, trend filters, volume analysis, or momentum indicators for increased conviction.
STARKPROFITS SCALPER 2.0señales compra y venta..tendencia y estructura del mercado.se basa en tendencia
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.
Carlos Money Printer (CMP 4.5)⭐ Carlos Money Printer (CMP 4.5) – Overview
Designed for precision day trading, swing filtering, and high-accuracy scalping.
Carlos Money Printer (CMP) 4.5 is a next-generation trading system engineered to identify high-probability trend expansions and disciplined exits using a multi-layer confirmation engine. CMP is built for traders who want clean visual signals, reduced noise, and a systematic approach that avoids emotional decision-making.
What CMP 4.5 Does
CMP analyzes market structure across multiple dimensions and automatically highlights:
🔥 1. High-Accuracy Entry Zones
CMP detects early-stage price expansions using a proprietary volatility engine (“BAM” signals) plus directional confirmation, giving traders visibility into explosive trend opportunities before most indicators react.
📈 2. Trend Direction & Strength
CMP reads trend behavior using a dynamic trend spine, allowing the system to clearly distinguish between pullbacks, trend continuation, and early reversal conditions.
🧠 3. Multi-Timeframe Confirmation
The built-in 6-timeframe dashboard shows whether higher-timeframes agree with the chart you're trading — giving you a fast snapshot of market alignment without flipping charts.
🎯 4. Sniper Entry System (Full/Moderate Modes)
CMP 4.5 offers two confluence-based entry models:
FULL Sniper Mode – highest confidence, strongest confluence
MOD Sniper Mode – more frequent entries with controlled risk
Both modes emphasize clean structure and avoid low-quality signals.
🚀 5. Intelligent Exit Engine (5m-Based)
CMP includes a hybrid exit model that combines:
Trend deceleration
Momentum reversal
Volatility exhaustion
Structural flip signals
This gives you objective, systematic exit points — no guessing, no chasing.
📊 6. Built-In Tools for Traders
ORB High/Low Zones (first 15 minutes)
ADR / ADT Daily Range Tracking
VWAP
Trend coloring
Clean chart-optimized visuals
Everything is integrated so you can trade from a single indicator.
🌟 Why Traders Like CMP
CMP is engineered to remove noise from the chart and show only the most useful information:
No clutter
No complicated settings
No lagging confirmation
No hype indicators
Just clean trend signals, controlled entries, and disciplined exits.
⚠️ Important Notice
CMP 4.5 is proprietary and licensed exclusively under the K&T Trust.
This is a private-use system intended for educational and non-commercial analysis.
Reproduction or redistribution of the source code is prohibited.
⚠️ Disclaimer
The Carlos Money Printer (CMP 4.5) indicator is a technical analysis tool designed for educational and informational purposes only. It does not constitute financial advice, investment advice, or trading recommendations. Like all trading indicators, CMP 4.5 cannot guarantee future performance, profitability, or accuracy. Markets involve risk, including the potential loss of capital.
By using CMP 4.5, the trader acknowledges and agrees that:
All trading decisions are made at their own risk,
Past performance does not guarantee future results,
CMP 4.5 is not a substitute for personal research or professional financial advice,
Neither the creator, K&T Trust, nor any affiliates are responsible for losses, damages, or outcomes resulting from its use.
CMP 4.5 is a tool — powerful, refined, and more advanced than many indicators — but it is not a promise, not a guarantee, and not liability-bearing.
Use it with proper risk management, discipline, and personal judgment.
BTC GOD — DEFINITIVE BTC MULTI INDICATORBTC GOD — The Ultimate Bitcoin Cycle Indicator (2025 Edition)
The one indicator every serious BTC holder and trader has been waiting for.
A single script that perfectly combines the 5 most powerful and accurate Bitcoin indicators ever created — all 100 % official versions:
- Official Pi Cycle Top (LookIntoBitcoin) → in 2013, 2017 & 2021 (3/3 hits)
- Official MVRV Z-Score (Glassnode / LookIntoBitcoin) → every major bottom (2015, 2018–19, 2022)
- Dynamic Bull/Bear background (red bear-market when price drops X % from cycle ATH + monthly RSI filter)
- Monthly Golden/Death Cross (50-month EMA vs 200-week EMA) → huge, unmistakable signals
- SuperTrend + 200-week EMA + 50-month EMA
- Cycle ATH/ATL tracking with flashing alert in the table when new highs/lows are made
- Exact days to/from the next halving + optimal accumulation zone (200–750 days post-halving)
- Fully customizable inputs for experienced traders
Zero repainting. Zero errors. Works on every timeframe.
This is the indicator used by people who truly understand Bitcoin’s 4-year cycles.
If you could only keep ONE Bitcoin indicator for the rest of your life… this would be it.
Save it, test it, and you’ll instantly see why it’s called BTC GOD.
Built with love and obsession for Bitcoin cycles.
Last update: November 2025
Painel de Probabilidade Multi-Timeframe Long ShortPainel de Probabilidade Multi-Timeframe for best possibility for Long ou Short
Price vs 200 EMA / 50 EMA / VWAP TablePrice vs 200 EMA / 50 EMA / VWAP Table
This indicator displays a compact real-time table showing where current price is trading relative to three major dynamic levels: 200 EMA, 50 EMA, and VWAP.
It provides an instant read on trend strength, bias, and distance from key moving levels — all in one glance.
Color-coded signals:
Lime → Price above
Red → Price below
Gray → Price at / near
Features
Adjustable table position (4 corners)
Adjustable text size
Toggle % distance and points distance
Clean, lightweight, and non-intrusive
Works on all timeframes and assets
13 / 26 / 52 SMA Overlay13 / 26 / 52 SMA Overlay showing how short term is performing relative to long term.
Institutional Buying %This is an Institutional Footprint Detector that identifies when large traders (institutions, hedge funds, market makers) are actively accumulating or distributing. Unlike retail-focused indicators, it detects the specific signatures institutions leave in the market:
Absorption (high volume, low movement)
Liquidity grabs (stop hunts)
Volume delta (buying vs selling pressure)
Hidden divergences (smart money disagreeing with price)
What it catches: Sustained institutional accumulation
Directional conviction with volume
When smart money is aggressively buying/selling
Divergences:
Hidden bullish div: Price makes lower low, but delta makes higher low
Translation: "Price falling but institutions secretly buying"
Hidden bearish div: Price makes higher high, but delta makes lower high
Translation: "Price rising but institutions secretly selling"
Absorption
Example: Price at support: $100
Volume: 3x average
Range: Only $0.50 movement
Close up → Bullish absorption (institutions eating supply)
What it catches:
Institutions absorbing supply without moving price
Stealth accumulation at support
Distribution at resistance
Classic "they're loading the boat" behavior
ATR-adaptive zones: Works on crypto, stocks, futures automatically
Liquidity Grabs
Example: Recent low: $98
Price spikes to $97.50 (breaks low, triggers stops)
Strong wick recovery, closes at $99.50
Bullish grab → Institutions hunted stops, now buying
Filters: Wick must be >1.2x opposite wick (real rejection)
Range expansion (filters inside bars)
Volume confirmation
This is pure market manipulation detection
Higher timeframe institutional flow Confirmation
Purpose:
Prevents trading against the institutional trend
Acts as a confirmation filter, not primary driver
"Don't fight the bigger money"
Adjustable: 5% for pure signal, 15% for strong trend following
How to Read the Signals
The Histogram (Main Display)
Green Zone (>65%): Strong institutional buying
All 4 components aligned bullish
Safe to be long-biased
Look for entries on pullbacks
Orange Zone (35-65%): Neutral/Consolidation
Mixed signals
Institutions not committed
Wait for clarity
Red Zone (<35%): Strong institutional selling
All 4 components aligned bearish
Reduce longs, consider shorts
Institutions distributing
Background Highlights
Lime Background: Bullish divergence detected
Hidden accumulation happening
Price may be about to reverse up
Major signal - institutions disagree with price decline
Red Background: Bearish divergence detected
Hidden distribution happening
Price may be about to reverse down
Major signal - institutions disagree with price rally
Optional: Cumulative Delta Line
Shows session-level institutional flow:
Rising line → Net buying pressure this session
Falling line → Net selling pressure this session
Resets daily (or your chosen session boundary)
Use: Confirms the histogram direction with intraday flow
How to Trade With It
Setup 1: Divergence + Absorption (Highest Probability)
Wait for divergence background (lime or red)
Check if absorption is occurring (enable debug plot for absorption Percent)
Enter when histogram crosses into green/red zone
Example: Price falling, making lower lows
Lime background appears (bullish divergence)
Histogram crosses above 65%
Entry: Go long, institutions are accumulating
Setup 2: Liquidity Grab Reversal
Price breaks obvious support/resistance
Strong wick rejection appears
Histogram confirms direction (green for bullish grab, red for bearish)
Example:
Price breaks $100 support, hits $99
Long lower wick, closes $101
Histogram >65% green
Entry: Long, stop hunt complete
Setup 3: HTF Alignment (Trend Following)
Set HTF to 240min or Daily
Increase HTF weight to 10-15%
Only trade when histogram aligns with HTF
Example: Daily timeframe shows strong accumulation
On 15min chart, wait for histogram >65%
Entry: Long on any green bar
Setup 4: Session Reset Play (Day Traders)
Enable cumulative delta plot
At session open, watch for delta direction
Enter when histogram confirms
Example: Market opens
Cumulative delta immediately spikes positive
Histogram moves into green zone
Entry: Long, institutions showing hand early
Best Practices
✅ DO: Wait for histogram to cross thresholds clearly
Trust divergences - they're ±35 point boosts for a reason
Use HTF as confirmation filter, not primary signal
Tune divergence sensitivity per instrument
Combine with price action at key levels
❌ DON'T: Trade in orange zone (institutions not committed)
Ignore divergence backgrounds (major signals)
Fight histogram when it's strongly green/red
Use on extremely illiquid assets
Enable all debug plots on 1min charts (lag)
This indicator gives you institutional x-ray vision. When the histogram is green, the big money is buying. When it's red, they're selling. The divergences show you when they're doing it secretly. Trade with them, not against them.
The label on the price scale shows the current Institutional Buying Percentage - it's a real-time reading of the indicator value.
What the Number Means
The label displays a value between 0 and 100:
Example readings:
75 (Green) → Institutions are strongly buying 75% buying pressure vs 25% selling pressure
All components (delta, absorption, liquidity, HTF) aligned bullish
Safe to be long-biased
50 (Orange) → Neutral/Balanced Equal buying and selling pressure
Institutions not committed either way
Wait for clarity before entering
25 (Red) → Institutions are strongly selling 25% buying pressure vs 75% selling pressure
All components aligned bearish
Reduce longs, consider shorts
Smart OI & Funding + Market RefSmart OI & Funding + Market Ref is a professional-grade tool designed for crypto perpetual traders who need accurate, real-time sentiment data without the hassle of manual configuration. It solves the common "Symbol Not Found" and NaN errors by using an intelligent symbol detection engine.
This indicator plots the Open Interest (OI) and Funding Rates for your current chart while simultaneously monitoring the broader market sentiment by displaying real-time funding rates for BTC and ETH on the dashboard.
Key Features
🧠 Smart Symbol Detection : automatically detects your current exchange (Binance, Bybit, OKX, etc.) and tries multiple ticker formats (e.g., .P, _OI, _FR) to find valid data. No more manual ticker searching.
📊 Dual-Pane Visualization :
Open Interest (Line): Displayed as a smoothed line area to visualize market participation and trend strength.
Funding Rate (Columns): Color-coded columns (Teal/Red) to instantly spot bullish or bearish sentiment extremes.
⚡ Real-Time Dashboard : A clean, non-intrusive table in the top-right corner displays:
Current Stats : Exact OI (formatted in Millions/Billions) and Funding Rate % for the coin you are viewing.
Market Reference : Live Funding Rates for BTCUSDT and ETHUSDT from your specific exchange to use as a baseline for market sentiment.
How to Use
1. Add to Chart: Apply the indicator to any Crypto Perpetual Futures chart (e.g., BTCUSDT.P, SOLUSDT.P).
2. Scale Setup (Important): Since Open Interest (Millions) and Funding Rates (0.01%) have vastly different values, you must separate their scales:Right-click the Blue Line (OI) $\rightarrow$ Select Pin to Scale Right.Right-click the Columns (Funding) $\rightarrow$ Select Pin to Scale Left.
3. Interpret the Data:High OI + Positive Funding: Strong Bullish sentiment (Longs paying Shorts). Watch for Long Squeezes.High OI + Negative Funding: Strong Bearish sentiment (Shorts paying Longs). Watch for Short Squeezes.Dashboard Ref: Compare your coin's funding to BTC/ETH. If your coin has 0.1% funding while BTC is 0.01%, your crypto is significantly "hotter" than the market average.
Universal Pivot ScannerUniversal Pivot Scanner
Professional pivot pattern detection for any market data source.
A robust pivot detector designed to work across all timeframes and data types - price action, technical indicators, volume, or custom studies. One tool, multiple applications.
Core Functionality
Identifies two high-probability retracement patterns:
HH+HL → Bullish continuation setup (higher high followed by higher low)
LL+LH → Bearish continuation setup (lower low followed by lower high)
Key Features
Non-repainting detection. Labels and alerts trigger at pivot confirmation, ensuring real-time actionable signals without historical bias.
Source flexibility. Compatible with any input - price data, oscillators (RSI, MACD), volume analysis, or proprietary indicators. Single implementation across multiple strategies.
Adaptive configuration. Adjustable lookback period optimizes performance for different market conditions and data types.
Recommended Settings
Lookback: 1 → Oscillators and momentum indicators
Lookback: 3-10 → Price action and ranging markets
Includes visualization table displaying recent pivot sequence and active pattern status.
Designed for systematic traders requiring consistent, verifiable signals across diverse market conditions.
Advanced Custom Volume Profile [KRUTO]⚠️ LANGUAGE NOTICE: This script features a SLOVAK (SK) user interface (settings and tooltips).
This is a highly customizable and versatile Volume Profile indicator designed for precise market analysis. It separates itself from standard tools by offering dynamic anchoring modes, advanced HVN/LVN detection logic, and a "Smart Lines" feature that keeps your chart clean.
Key Features
1. Three Dynamic Anchoring Modes:
Fixed Range (Na čiare výberu): Define exact Start and End times manually. Includes vertical dashed lines to visualize the range.
Anchor to Last Candle (Na poslednej sviečke): Calculates volume from a specific start time up to the current live price. The profile is always anchored to the most recent bar.
Visible Range (Visible - Viditeľné sviečky): Dynamically calculates the profile based only on the candles currently visible on your screen. As you scroll or zoom, the profile updates automatically.
2. HVN & LVN Detection:
HVN (High Volume Nodes): Automatically highlights areas of high consolidation (Green zones). Includes a "merge tolerance" setting to group nearby nodes.
LVN (Low Volume Nodes): Highlights areas of low liquidity/rejection (Red zones).
3. Key Levels & Visuals:
Displays POC (Point of Control), VAH (Value Area High), and VAL (Value Area Low) with extendable lines.
Smart Offset: Keeps the profile at a fixed distance from the latest candle (or right edge) to prevent chart clutter.
Clean Look: Vertical range lines automatically disappear when not in "Fixed Range" mode.
Translation Guide (Slovak -> English)
Since the settings are in Slovak, here is a quick guide for English users:
Zdroj dát profilu (Source):
Na čiare výberu = Fixed Time Range
Na poslednej sviečke = Fixed Start to Current Bar
Visible = Visible Range
Počet úrovní (Bins): Resolution of the histogram (e.g., 160).
Value Area (%): Percentage of volume considered as value (Standard 70%).
Začiatočný / Koncový čas: Start / End Time.
Offset: Distance of the profile from the price action.
Zobraziť HVN / LVN: Show High/Low Volume Nodes.
Credits: Custom logic developed for advanced volume analysis with anti-overlap algorithms for node visualization.
Enjoy the script! 🚀
IBD Style RS Rating Line IndicatorPurpose
Measures relative performance, not just price action.
Recreates the IBD-style 1–99 RS Rating inside TradingView.
RS Line
Plots stock price relative to a benchmark (default: SPX).
Scaled for readability; supports indices and sectors.
Optional MA overlays and positive/negative fill zones.
RS New Highs / New Lows
Scans a user-defined lookback.
Marks RS new highs (blue) and new lows (red).
Modes for historical, last-bar-only, or “RS leads price.”
RS Rating (1–99)
Calculates a weighted performance score over 1–12 months.
Compares this score to market-wide thresholds pulled via request.seed().
Converts score into percentile bands (e.g., 70–89, 90–98).
Assigns 99 to top leaders and 1 to laggards.
Fallback Logic
Missing environment data = shows “RS” without a number.
Replay mode uses fixed thresholds to approximate ratings.
Output
Clean label showing RS Rating near the RS line.
Helps traders instantly judge whether a stock is a true leader.
Kalman Trend Sniper# KALMAN TREND SNIPER
## ORIGINALITY STATEMENT
The Kalman Trend Sniper combines adaptive trend detection with precision entry validation to identify high-probability trading opportunities. Unlike static moving averages that use fixed parameters, this indicator adapts to changing market volatility through ATR-based gain adjustment and distinguishes trending from ranging markets using ADX regime detection.
The indicator's unique contribution is its three-phase entry validation system: signals must hold for three bars, undergo a pullback test to the signal level, and receive confirmation through price action before generating an entry. This structured approach helps traders enter established trends at favorable retracement levels rather than chasing momentum.
---
## TECHNICAL METHODOLOGY
### Kalman Filter Implementation
This indicator implements an Alpha-Beta variant of the Kalman filter, a recursive algorithm that estimates trend from noisy price data:
1. Prediction: kf = kf + velocity
2. Error calculation: error = price - kf
3. Correction: kf = kf + gain * error
4. Velocity update: velocity = velocity + (gain * error) / 2
The gain parameter determines filter responsiveness. Higher gain values track price more closely but increase noise sensitivity, while lower values provide smoother output but lag price changes.
### Adaptive Gain Mechanism
The indicator adjusts gain dynamically based on volatility:
Volatility Factor = Current ATR / Long-term ATR
Adaptive Gain = Base Gain * (0.7 + 0.6 * Volatility Factor)
This ATR ratio increases responsiveness during high-volatility periods and reduces sensitivity during consolidations, addressing the fixed-parameter limitation of traditional moving averages. The volatility factor is bounded between configurable minimum and maximum values to prevent extreme adjustments.
### Regime Detection
The indicator uses the Average Directional Index (ADX) to distinguish market conditions:
- Trending markets (ADX above threshold): Full gain applied, signals generated
- Ranging markets (ADX below threshold): Gain reduced 25%, fewer signals
This regime awareness helps reduce whipsaw signals during sideways consolidation periods.
### Signal Line Validation System
When the Kalman line changes direction in trending conditions, the indicator draws a horizontal signal line at the low (for long signals) or high (for short signals) of the signal candle. This line represents a potential support or resistance level.
The validation system then monitors three phases:
Phase 1 - Hold Period: Price must remain above (long) or below (short) the signal line for three consecutive bars. This requirement filters weak signals where price immediately violates the signal level.
Phase 2 - Test: After the hold period, the system waits for price to pull back and touch the signal line, with configurable tolerance for volatile instruments.
Phase 3 - Confirmation: Within eight bars of the test, a confirmation candle must close above (long) or below (short) the test candle's body, demonstrating renewed momentum. If confirmation does not occur within eight bars, the validation attempt expires.
Successful validation generates an R label at the entry point. This three-phase structure helps identify entries where trend direction and support/resistance validation align.
---
## USAGE INSTRUCTIONS
### Signal Interpretation
Triangle Signals:
- Upward triangle (teal): Kalman line turns bullish in trending market (ADX above threshold)
- Downward triangle (red): Kalman line turns bearish in trending market
Signal Lines (horizontal):
- Teal line: Potential long support level at signal candle low
- Red line: Potential short resistance level at signal candle high
- Gray line: First opposite-color candle after signal (initial reversal pressure)
R Labels (optional, disabled by default):
- Green R below price: Validation complete for long entry
- Red R above price: Validation complete for short entry
Stop Levels:
- Red dots: Long stop level (Kalman line minus ATR multiplier)
- Teal dots: Short stop level (Kalman line plus ATR multiplier)
### Dashboard Information
The dashboard displays real-time indicator state:
- Trend: Current Kalman direction (BULL/BEAR)
- Regime: Market classification (Trending when ADX exceeds threshold, Ranging otherwise)
- Gain: Current adaptive gain value
- Vol Factor: Volatility ratio (current ATR / long-term ATR)
- ADX: Trend strength (higher values indicate stronger trends)
- Z-Score: Standard deviation distance from Kalman line (when enabled)
- Stop Dist: Current ATR-based stop distance
- Lines: Number of active signal lines displayed
- R-Status: Validation system state (Idle / Waiting / Testing)
### Trading Applications
Trend Following Approach:
1. Wait for triangle signal in trending market (ADX above threshold)
2. Enter immediately at signal candle close or wait for pullback
3. Place stop at displayed stop level
4. Trail stop using Kalman line as dynamic support/resistance
Validation Entry Approach (conservative):
1. After triangle signal, observe three-bar hold period
2. Wait for pullback to signal line (test phase)
3. Enter on R label confirmation
4. Place stop below/above signal line
5. Provides higher probability entries but reduces trade frequency
Z-Score Mean Reversion (when enabled):
1. Watch for Z-Score exceeding entry threshold (default +/-2.0)
2. Consider counter-trend entries when price touches Kalman line
3. Target return to Kalman line (Z-Score near zero)
4. Use Z-Score threshold as stop level for extreme continuation
### Optimal Conditions
The indicator performs optimally in clearly trending markets where ADX consistently exceeds the threshold. Performance degrades in sideways, choppy conditions.
Recommended timeframes:
- 1-5 minute charts: Use Crypto_1M preset (faster adaptation)
- 15-60 minute charts: Use Crypto_15M preset (balanced)
- Hourly charts: Use Forex preset (smoother)
- Daily charts: Use Stocks_Daily preset (long-term trends)
Market conditions:
- High volatility (Vol Factor above 1.5): Expect faster adaptation, wider stops needed
- Normal volatility (Vol Factor 0.7-1.5): Standard behavior
- Low volatility (Vol Factor below 0.7): Expect slower adaptation, tighter stops possible
---
## PARAMETER DOCUMENTATION
### Kalman Filter Settings
Preset Mode: Select optimized configuration for specific markets
- Custom: Manual parameter control
- Crypto_1M: Base Gain 0.05, ATR 7 (fast response for 1-5 minute crypto charts)
- Crypto_15M: Base Gain 0.03, ATR 14 (balanced for 15-60 minute crypto charts)
- Forex: Base Gain 0.02, ATR 14 (standard for forex pairs)
- Stocks_Daily: Base Gain 0.01, ATR 20 (smooth for daily stock charts)
Base Gain (0.001-0.2): Core Kalman filter responsiveness parameter. Higher values increase sensitivity to price changes. Low values (0.01-0.02) provide smooth output with fewer whipsaws but slower trend changes. High values (0.06-0.08) offer fast response with more signals but increased whipsaw risk.
Adaptive (checkbox): When enabled, automatically adjusts gain based on ATR ratio. Recommended to keep enabled for dynamic volatility adaptation.
ATR (5-50): Short-term Average True Range period for current volatility measurement. Default 14 is industry standard. Lower values respond faster to volatility changes.
Long ATR (20-200): Long-term ATR period for baseline volatility comparison. Default 50 provides stable reference. The ratio between ATR and Long ATR determines adaptive adjustment magnitude.
Regime Filter (checkbox): Enables ADX-based trending/ranging detection. When enabled, reduces gain by 25 percent during ranging markets to minimize false signals.
ADX Period (7-30): Period for ADX calculation. Default 14 is standard. Lower values respond faster to trend strength changes.
Threshold (15-40): ADX level distinguishing trending from ranging markets. Default 25. Above threshold: trending (generate signals normally). Below threshold: ranging (reduce sensitivity).
Min Vol / Max Vol (0.3-3.0): Bounds for volatility factor adjustment. Prevents extreme gain changes during unusual volatility spikes or quiet periods. Default minimum 0.5, maximum 2.0.
Stop ATR x (1.0-3.0): Multiplier for ATR-based stop loss distance. Default 2.0 places stops two ATRs from Kalman line. Use 1.5 for tight stops (intraday), 2.5-3.0 for wide stops (swing trading).
Show Signals (checkbox): Displays triangle signals when Kalman changes direction in trending markets. Disable to use indicator purely as dynamic support/resistance without signals.
Z-Score (checkbox): Enables mean-reversion signal generation based on statistical deviation from Kalman line.
Period (10-100): Lookback period for Z-Score standard deviation calculation. Default 20 bars. Longer periods produce smoother, less sensitive readings.
Entry (1.5-3.5): Standard deviation threshold for Z-Score signals. Default 2.0 generates signals at plus/minus two standard deviations (approximately 95th percentile moves).
Bull / Bear Colors: Customize Kalman line colors for uptrend (default teal) and downtrend (default red).
Fill (checkbox): Shows semi-transparent fill between price and Kalman line for visual trend emphasis.
### Signal Line System Settings
Signal Lines (checkbox): Displays horizontal signal lines at low (long) or high (short) of signal candles. These function as dynamic support/resistance levels.
Reverse Lines (checkbox): Shows gray horizontal lines at first opposite-colored candle after signal. Helps identify initial resistance points in new trends.
Max Lines (0-20): Maximum number of signal lines to display simultaneously. Older lines are removed as new signals appear. Use 1-2 for clean charts, 3-5 for recent support/resistance history.
Style (Solid/Dotted/Dashed): Visual style for signal and reverse lines. Dotted provides subtle appearance, solid is most prominent.
Line % / Label % (0-100): Transparency percentage for lines and labels. Zero is fully opaque, 100 is invisible.
R Labels (checkbox): Shows R labels when validation confirmation occurs. Default disabled. Enable if you want visual confirmation of successful pullback entries.
Tolerance % (0-1.0): Price deviation tolerance for test candle detection. Zero requires exact touch. 0.5 allows 0.5 percent deviation for volatile instruments.
### Dashboard Settings
Show Dashboard (checkbox): Toggles visibility of information panel. Disable for clean chart presentation.
Position: Choose dashboard location from nine positions (Top/Middle/Bottom combined with Left/Center/Right).
---
## LIMITATIONS AND WARNINGS
This indicator is a technical analysis tool that processes historical price data. It does not predict future price movements.
Inherent limitations:
1. Lagging nature: Like all trend indicators, the Kalman filter lags price. Signals occur after trend changes begin, not before.
2. Ranging markets: Generates fewer signals and reduced performance when ADX falls below threshold. Not optimized for sideways consolidation.
3. Whipsaw risk: In choppy, indecisive markets near ADX threshold, signals may reverse quickly despite regime filtering.
4. Parameter sensitivity: Inappropriate Base Gain settings can cause over-trading (too high) or missed trends (too low).
5. Validation requirement: The three-phase confirmation system provides higher accuracy but significantly reduces trade frequency. Not all trends produce valid pullback entries.
Not suitable for:
- Scalping strategies requiring instant signals (Kalman filter has intentional smoothing)
- Ultra-high frequency trading (indicator updates once per bar close)
- Markets with extreme overnight gaps (stops may be exceeded)
- Strategies requiring signals on Heikin Ashi, Renko, Kagi, Point and Figure, or Range charts
Risk management requirements:
This indicator provides trend direction and signal levels but does not incorporate position sizing, risk management, or account balance considerations. Users must implement appropriate position sizing, maximum daily loss limits, and portfolio diversification. Past performance does not indicate future results.
Optimal usage:
- Works optimally in clearly trending markets where ADX consistently exceeds threshold
- Performance degrades in sideways, choppy conditions
- Designed for swing trading and position trading timeframes (15-minute and above)
- Requires confirmation from price action or additional technical analysis
---
## NO REPAINT GUARANTEE
This indicator operates on bar close confirmation only. All signals, signal lines, and validation labels appear exclusively when candles close. Historical signals remain exactly where they appeared. This makes the indicator suitable for automated trading and reliable backtesting. What you see in historical data matches what appeared in real-time.
---
## ALERTS
The indicator provides eight alert conditions:
1. Kalman Buy Signal: Fires when upward triangle appears (bullish trend change in trending market)
2. Kalman Sell Signal: Fires when downward triangle appears (bearish trend change in trending market)
3. Trend Change to Bullish: Fires whenever Kalman line changes to bullish (regardless of ADX)
4. Trend Change to Bearish: Fires whenever Kalman line changes to bearish (regardless of ADX)
5. SCT-R Long Retest Confirmed: Fires when green R label appears for long validation
6. SCT-R Short Retest Confirmed: Fires when red R label appears for short validation
7. SCT Test Long Detected: Fires when test candle appears for long signal (before confirmation)
8. SCT Test Short Detected: Fires when test candle appears for short signal (before confirmation)
Alert messages include context about bar close confirmation and current price levels.
---
## CALCULATION TRANSPARENCY
While complete proprietary optimization methodology is not disclosed, the core technical approach is fully explained: Alpha-Beta Kalman filter with ATR-based adaptive gain adjustment and ADX regime detection. The signal line validation system uses a three-phase structure (hold, test, confirmation) with configurable parameters. Users can understand indicator functionality and make informed decisions about application.
---
## DISCLAIMER
This indicator is provided as a technical analysis tool. It does not constitute financial advice, trading recommendations, or performance guarantees. All trading decisions carry risk. Users are responsible for their own trading decisions and risk management. Past results do not indicate future performance.
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.
Trend Line Proximity Meter (Improved v2 with Recent Touches)Overview
Trend Line Proximity Meter (Improved v2 with Recent Touches) is a powerful overlay tool that transforms any manually-defined trend line (via two configurable points) into a live analytical instrument. It draws the line, auto-detects whether it currently acts as support or resistance, and calculates real-time metrics: % deviation, absolute distance, projected price, slope, historical touch count, and — new in v2 — recent touch count within a user-defined lookback. All data appears in a clean, color-coded dashboard that updates instantly as price moves. Perfect for traders who draw their own channels or trend lines and want objective, quantitative feedback without clutter.
Core Mechanics
Trend Line Creation: Define two points by "bars back" and price type (High/Low/Close/Open/Custom). The script builds the line from those coordinates and optionally extends it as a ray.
Dynamic Role Detection: Automatically classifies the line as Support (price above), Resistance (price below), or Neutral. Auto-colors the line and dashboard accordingly when enabled.
Proximity Engine:
% Deviation: ((close – line price) / line price) × 100
Absolute Distance: close – line price
Projected Price: Exact line value at current bar
Slope: Price change per bar
Touch Counting (v2 Enhancement):
Historical Touches: Total times any candle’s range intersected the line within its drawn/extended segment.
Recent Touches: Same logic but limited to the last N bars (default 50) — instantly shows if the level is “hot” right now.
Tolerance % (default 0.2%) accounts for wicks and minor breaches.
Dashboard: Fully configurable position/size. Displays all metrics with intuitive color coding:
Green/Yellow/Red proximity zones (user-defined thresholds)
Support (green) / Resistance (red) auto-highlighting
Recent vs Historical touch split for quick context
Why This Adds Value & Originality
Most trend line tools only draw lines. This is the only public script that turns a single user-drawn trend line into a full analytical dashboard with real-time deviation %, projected price, slope, and dual touch counters (historical + recent). The recent-touch feature instantly reveals whether a level is currently respected or ignored — information no other indicator provides automatically. Clean, efficient code (no lookahead, no repainting) and smart proximity coloring make it uniquely practical for discretionary traders who rely on hand-drawn lines but want objective data.
How to Use
Draw Your Line:
Set Point 1 (e.g., 100 bars back → Low)
Set Point 2 (e.g., 50 bars back → High)
Adjust style, width, and whether to extend.
Interpret the Dashboard:
Line Type: “SUPPORT” (green) or “RESISTANCE” (red)
Proximity %: +0.4% = price 0.4% above line (green zone = very close)
Recent Touches (Last 50): 4 → level is active now
Historical Touches: 12 → proven significance
Trade Ideas:
Price near line + high recent touches → watch for bounce/rejection
Break with low recent touches → potential trend change
Use alerts for “Price Near Trend Line” or crossovers
Best Practice: Use on 1H–Daily charts; combine with volume or order flow for confluence.
Limitations & Disclaimer
Line is based on two fixed historical points — moving markets may require occasional re-adjustment. Touch detection uses a small tolerance zone (adjustable). No automatic multi-line support (one line per instance). Not financial advice — use with proper risk management. Past performance ≠ future results. Questions? Comment below!
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.
Hash Supertrend [Hash Capital Research]Hash Supertrend Strategy by Hash Capital Research
Overview
Hash Supertrend is a professional-grade trend-following strategy that combines the proven Supertrend indicator with institutional visual design and flexible time filtering.
The strategy uses ATR-based volatility bands to identify trend direction and executes position reversals when the trend flips.This implementation features a distinctive fluorescent color system with customizable glow effects, making trend changes immediately visible while maintaining the clean, professional aesthetic expected in quantitative trading environments.
Entry Signals:
Long Entry: Price crosses above the Supertrend line (trend flips bullish)
Short Entry: Price crosses below the Supertrend line (trend flips bearish)
Controls the lookback period for volatility calculation
Lower values (7-10): More sensitive to price changes, generates more signals
Higher values (12-14): Smoother response, fewer signals but potentially delayed entries
Recommended range: 7-14 depending on market volatility
Factor (Default: 3.0)
Restricts trading to specific hours
Useful for avoiding low-liquidity sessions, overnight gaps, or known choppy periods
When disabled, strategy trades 24/7
Start Hour (Default: 9) & Start Minute (Default: 30)
Define when the trading session begins
Uses exchange timezone in 24-hour format
Example: 9:30 = 9:30 AM
End Hour (Default: 16) & End Minute (Default: 0)
Controls the vibrancy of the fluorescent color system
1-3: Subtle, muted colors
4-6: Balanced, moderate saturation
7-10: Bright, highly saturated fluorescent appearance
Affects both the Supertrend line and trend zones
Glow Effect (Default: On)
Adds luminous halo around the Supertrend line
Creates a multi-layered visual with depth
Particularly effective during strong trends
Glow Intensity (Default: 5.0)
Displays tiny fluorescent dots at entry points
Green dot below bar: Long entry
Red dot above bar: Short entry
Provides clear visual confirmation of executed trades
Show Trend Zone (Default: On)
Strong trending markets (2020-style bull runs, sustained bear markets)
Markets with clear directional bias
Instruments with consistent volatility patterns
Timeframes: 15m to Daily (optimal on 1H-4H)
Challenging Conditions:
Choppy, range-bound markets
Low volatility consolidation periods
Highly news-driven instruments with frequent gaps
Very low timeframes (1m-5m) prone to noise
Recommended AssetsCryptocurrency:
