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Previous Day Week High Low EQ Extensions FIB BoxPDH / PDL EQ 25–75 Indicator PDH / PDL EQ 25–75 is a comprehensive market-structure and range-analysis indicator designed to visualize key daily and weekly reference levels directly on the chart. The script automatically plots prior highs and lows, equilibrium levels, range-based extensions, Fibonacci zones, and session opens, providing traders with a structured framework for directional bias, mean reversion, and liquidity-based analysis. Core Features Daily Range Framework Previous Day High (PDH) and Previous Day Low (PDL) Daily Equilibrium (50%) of the prior day’s range 25% and 75% Quartile Levels for range segmentation Range Extensions at ±25% and ±50% beyond PDH/PDL Daily Open (DO) plotted and extended forward Fibonacci Discount/Premium Zone (61.8%–78.6%) highlighted with a shaded box These levels are recalculated at the start of each trading day and extended forward for clear intraday reference. Weekly Range Framework Previous Week High (PWH) and Previous Week Low (PWL) Weekly Equilibrium (50%) Weekly Fibonacci Discount/Premium Zone (61.8%–78.6%) Weekly Open (WO) plotted and extended Weekly levels reset automatically at the start of each new trading week and are maintained independently from daily levels. Visual & Customization Options Fully configurable colors, line widths, and line styles for every plotted level Adjustable forward extensions for range and open levels Optional labels with customizable size and optional price display Distinct separator lines marking daily and weekly ranges Independent toggles for: Extension levels Fibonacci zones Labels The indicator is optimized for clarity while maintaining flexibility for different trading styles and chart layouts. Technical Implementation Highlights Uses higher-timeframe data via request.security() to ensure accurate daily and weekly calculations Automatically anchors PDH, PDL, PWH, and PWL to their true originating bars Efficient object management using arrays to prevent clutter and maintain platform performance Designed for overlay use on any intraday or higher-timeframe chart Use Cases Identifying premium and discount zones Mapping mean-reversion and continuation areas Tracking institutional reference levels Intraday trading with higher-timeframe context Futures, forex, crypto, and equity markets
Pine Script® indicator
by IdleAli
Gann Octave Pro - Angles & Time Cycles 🎯 Gann Octave Pro - Angles & Time Cycles ## Complete Gann Trading System - Price, Angles & Time in One Indicator A professional-grade Gann analysis tool combining **Octave Price Levels**, **Gann Angles (1x1, 2x1, 1x2)**, and **Advanced Time Cycle Projections**. Perfect for traders seeking precision market timing through geometric confluence. --- ## 🌟 Key Features ### 📐 Octave Price Levels - **5 Key Levels**: 0%, 25%, 50%, 75%, 100% - **Color-Coded**: Green (support) → Blue (50% pivot) → Red (resistance) → Black (boundaries) - **Dynamic Updates**: Auto-adjusts to swing structure - **Trading Edge**: 50% level is the most powerful reversal zone ### 📏 Gann Angles - **1x1 Angle** (Black) - Natural 45° trend line - **2x1 Angle** (Red) - Steep acceleration zone - **1x2 Angle** (Red) - Gradual support/resistance - **Customizable Extension**: Fixed bars or % of swing length ### ⏰ Advanced Time Cycles **Three Calculation Methods:** 1. **Angle-Level Confluence** ⭐ (Recommended) - Calculates intersections of Gann angles with octave levels - Most sophisticated timing system - Based on price-time geometry 2. **Swing Duration** - Uses actual swing bar length 3. **Harmonic (Swing/8)** - Classic Gann harmonic division **Cycle Visualization:** - **Full Cycles** (Purple, solid) - Major turning points, labeled "◆ FC1 (176 bars) " - **Sub-Cycles** (Blue, dotted) - Minor pivots, labeled "S1 " - **Mid-Cycles** (Orange, dashed) - Half-cycle inflection points - **Past Display**: Shows 4 complete past cycles for validation - **Future Projection**: Projects 8 future cycles for anticipation --- ## 🎯 How to Use ### Quick Start 1. Apply to chart (works all timeframes/instruments) 2. Select period: Default 44 bars (adjust based on timeframe) 3. Choose cycle method: "Angle-Level Confluence" for best results 4. Observe past cycles to validate timing accuracy ### Trading Strategies **Triple Confluence Setup** (Highest Probability) - Price at octave level (especially 50%) - Price touches Gann angle (1x1 most reliable) - Time cycle arrives (full cycle preferred) - **Entry**: On confluence | **Stop**: Below/above octave level | **Target**: Next level **Cycle Anticipation** - Enter 1-2 bars before cycle line if price at octave level - Exit at next cycle or target octave level - **Edge**: Anticipate cycles instead of reacting **Angle Breakout + Cycle** - Price breaks 1x1 angle + next cycle within 20 bars - Hold through cycle, exit at 2x1 angle or next major level --- ## ⚙️ Customization ### Period Selection (88-Based) 11 harmonic options: 3, 6, 11, 22, **44**, 88, 176, 352, 704, 1408, 2816 bars - **Intraday** (15m-1h): Period 3-4 - **Swing Trading** (4h-Daily): Period 4-5 - **Position Trading** (Daily-Weekly): Period 5-6 ### Visual Controls - **Colors**: Independent for all elements - **Line Widths**: Separate controls (1-5) for levels, angles, cycles - **Label Size**: Tiny/Small/Normal/Large (unified) - **Label Position**: Top/Middle/Bottom - **Show/Hide**: Toggle any component ### Alerts - 50% octave level breakouts - Customizable messages --- ## 💡 Pro Tips 1. **Validate First**: Observe 2-3 past cycles before trading 2. **Adjust to Volatility**: High volatility = lower period (22-44), Low = higher (88-176) 3. **Multiple Timeframes**: Apply on different timeframes for confirmation 4. **Respect 50% Level**: Most powerful reversal zone in Gann theory 5. **Focus on Full Cycles**: Highest probability setups (◆ FC markers) 6. **Combine with Price Action**: Indicator shows WHERE/WHEN, price action shows HOW --- ## 🚀 What Makes It Unique ✅ **Intelligent Confluence Cycles** - Unique angle-level intersection calculation ✅ **Historical Validation** - See past cycles to trust future projections ✅ **Professional Design** - Color-coded hierarchy, clean labels, no clutter ✅ **Complete Automation** - Everything updates in real-time ✅ **Three-Dimensional Analysis** - Price + Angles + Time = complete picture --- ## 📊 Best Markets - Stock indices (S&P 500, NASDAQ, Dow) - Forex majors (EUR/USD, GBP/USD, USD/JPY) - Commodities (Gold, Silver, Oil) - Crypto (BTC, ETH) - Liquid stocks ✅ Complete Gann system (price + angles + time) ✅ 3 time cycle methods ✅ Auto swing detection ✅ 4 past + 8 future cycle projections ✅ Professional visualization ✅ Extensive customization ✅ Real-time alerts ✅ Works all markets/timeframes --- ## ⚠️ Disclaimer This indicator is for educational purposes and applies W.D. Gann methodology principles. Not financial advice. Always use proper risk management, position sizing, and stop losses. Practice on paper before live trading. Past performance doesn't guarantee future results. --- **The market moves in patterns of price and time. This indicator helps you see them.** Trade with geometry. Trade with time. Trade with confidence.
Pine Script® indicator
by TimePriceDecoder
2
Medium-term TrendThis Medium-term Trend indicator is designed to identify short, mid, and long-term price pivots, track trend directions, and visualize key support and resistance zones. It excels at analyzing mid-term trends, the most optimal timeframe for traders, and delivers greater reliability when applied to larger chart periods. The indicator helps you dynamically observe the battle between bullish and bearish forces at mid-term highs and lows, enabling you to align your trades with the prevailing trend. How to Use This Script 1. Core Parameter Adjustment The only critical adjustable parameter for trend validation is Retrace Percentage (%).It defaults to 0.01, with a range of 0 to 20.0 (adjustable in 0.01 increments). This parameter defines the minimum retracement percentage required to confirm a trend change from bullish to bearish or vice versa. A higher value means a more conservative trend change confirmation (fewer false signals), while a lower value captures more frequent trend shifts (may include more noise). 2. Visual Display Controls (Toggle On/Off) You can enable or disable the following visual elements via the indicator settings panel to match your chart clarity needs. Pivot Point Displays Show Short Points: Disable by default. When enabled, small green circles mark short-term lows and small red circles mark short-term highs, with tooltips showing the exact pivot price. Show Mid Points: Enables by default. When enabled, tiny yellow circles mark mid-term lows and mid-term highs (the core of the indicator), with tooltips showing the exact pivot price. These points are key for identifying mid-term trend direction. Show Long Points: Disables by default. When enabled, small blue circles mark long-term lows and long-term highs, with tooltips showing the exact pivot price. Trend Channel Displays Show Short Channel: Disables by default. When enabled, green lines connect consecutive short-term lows and red lines connect consecutive short-term highs, forming a short-term price channel. Show Mid Channel: Disables by default. When enabled, yellow lines connect consecutive mid-term lows and mid-term highs, forming a mid-term price channel that clearly visualizes the mid-term trend trajectory. Show Long Channel: Disables by default. When enabled, blue lines connect consecutive long-term lows and long-term highs, forming a long-term price channel for broader trend analysis. Mid-term Pivot Rectangles (Core Visual Element) Show Mid Rectangles: Enables by default. When enabled, transparent rectangles mark mid-term pivot zones (support and resistance) with dynamic break tracking.These rectangles extend to the right until the trend completes, helping you monitor price interactions with key mid-term levels. 3. Trend Identification & Trading Guidance Key Trend Rules (Mid-term Focus) Uptrend Confirmation: When mid-term lows show a sequential upward pattern (each subsequent mid-term low is higher than the previous one), the mid-term trend is bullish (uptrend).Downtrend Confirmation: When mid-term highs show a sequential downward pattern (each subsequent mid-term high is lower than the previous one), the mid-term trend is bearish (downtrend).Range Bound Condition: When mid-term highs and lows move sideways (no clear upward/downward sequence), the market is in a mid-term range. 4.How to Align Trades with the Trend Observe Mid-term Pivot Interactions: Pay close attention to price reactions at the mid-term rectangles (purple for support, orange for resistance). These zones represent key battle areas between bulls and bears. Uptrend Trading: In a confirmed mid-term uptrend, prioritize long trades when price touches or bounces from mid-term support rectangles (purple), with stop losses placed below the support rectangle’s bottom edge. Downtrend Trading: In a confirmed mid-term downtrend, prioritize short trades when price touches or rejects from mid-term resistance rectangles (orange), with stop losses placed above the resistance rectangle’s top edge. Range Trading: In a mid-term range, trade between consecutive mid-term support (purple) and resistance (orange) rectangles—buy near support and sell near resistance, with tight stop losses beyond the rectangle edges. Trend Breakout Confirmation: When price closes beyond the top (uptrend breakout) or bottom (downtrend breakout) of a mid-term rectangle, and the rectangle stops extending, this signals a potential mid-term trend shift. Wait for a retest of the broken rectangle (if applicable) to enter trades in the direction of the breakout. 5. Best Practices Optimal Timeframes: While the indicator works on all timeframes, it performs best on larger periods (4-hour, daily, weekly) where mid-term trends are more defined and less prone to noise.Mid-term Focus: For consistent trading results, prioritize mid-term signals (yellow pivot points, mid rectangles) over short-term signals, as mid-term trends offer higher probability trades with favorable risk-reward ratios.Avoid Overcluttering: Keep short-term and long-term displays disabled by default unless you need multi-timeframe confluence. Enabling too many visual elements can obscure key mid-term trend signals.Parameter Fine-Tuning: Adjust the Retrace Percentage (%) based on your asset’s volatility—use higher values (e.g., 0.5 to 2.0) for volatile assets (cryptocurrencies) and lower values (e.g., 0.01 to 0.2) for less volatile assets (blue-chip stocks).Dynamic Analysis: Regularly monitor the evolution of mid-term pivot rectangles and pivot point sequences—trends are not static, and early detection of shifting mid-term highs/lows can help you exit losing trades and capture new trend opportunities. Disclaimer: This indicator is for educational and analytical purposes only. It does not constitute financial advice. Always conduct your own research and risk assessment before executing trades. For support or customization requests, please send a private message to the author.
Pine Script® indicator
by gary186
ADR % RangesThis indicator is designed to visually represent percentage lines from the open of the day. The % amount is determined by X amount of the last days to create an average...or Average Daily Range (ADR). 1. ADR Percentage Lines: The core function of the script is to apply lines to the chart that represent specific percentage changes from the daily open. It first calculates the average over X amount of days and then displays two lines that are 1/3rd of that average. One line goes above the other line goes below. The other two lines are the full "range" of the average. These lines can act as boundaries or targets to know how an asset has moved recently. *Past performance is not indicative of current or future results. The calculation for ADR is: Step 1. Calculate Today's Range = DailyHigh - DailyLow Step 2. Store this average after the day has completed Step 3. Sum all day's ranges Step 4. Divide by total number of days Step 5. Draw on chart 2. Customizable Inputs: Users have the flexibility to customize the script through various inputs. This includes the option to display lines only for the current trading day (`todayonly`), and to select which lines are displayed. The user can also opt to show a table the displays the total range of previous days and the average range of those previous days. 3. No Secondary Timeframe: The ADR is computed based on whatever timeframe the chart is and does not reference secondary periods. Therefore the script cannot be used on charts greater than daily. This script is can be used by all traders for any market. The trader might have to adjust the "X" number of days back to compute a historical average. Maybe they only want to know the average over the past week (5 days) or maybe the past month (20 days).
Pine Script® indicator
by GoodGains
4
D_H_L_OIndicator Name: D_H_L_O Primary Function: This indicator is designed to display buying pressure, selling pressure, and other key metrics derived from the daily candle on a TradingView chart. It helps you analyze market momentum, buying and selling forces, and price spreads. Features Overview: Basic Calculations from Daily Candle: dailyHigh, dailyLow, dailyOpen, dailyClose: Represent the high, low, open, and close prices of the daily candle. dailySpread: The difference between the high and low prices of the daily candle. Buying and Selling Pressure: Buying Pressure (high_open): The difference between the daily high and the open price. Selling Pressure (low_open): The absolute difference between the daily low and the open price (displayed as a negative value). deltaVolume: The net difference between buying and selling pressure. Color and Visuals: Blue (buyingColor): Indicates buying pressure for green (bullish) days. Orange (sellingColor): Indicates selling pressure for red (bearish) days. Displays bars with transparency to distinguish buying and selling forces. Neutral Reference Line: A horizontal line at 0 for quick visual comparison of buying and selling forces. Labels for Key Information: Displays values of buying pressure, selling pressure, and daily candle spread directly on the chart at corresponding bar positions. Includes the weekday name (currentWeekday) for additional time context. Historical Statistics: Highest and lowest values of buying and selling pressure across the dataset. Average buying and selling pressure. Displays statistical summaries (like maximum pressure values) as labels on the last bar of the chart. Benefits: Detailed Market Pressure Visualization: Provides a clear view of the forces driving market movement each day. Historical Context: Helps analyze historical trends in buying and selling pressures over time. Decision-Making Support: Use pressure metrics to gauge market momentum and assess potential trends. How to Use: Copy and paste the script into TradingView (create a new indicator using Pine Script v5). Add the indicator to your chart on any timeframe to observe daily candle metrics. Customize colors, transparency, or other parameters to suit your trading style. This indicator is ideal for traders who want to analyze price momentum and make decisions based on daily market behavior.
Pine Script® indicator
by ZACH_FOREX
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.
Pine Script® indicator
by DskyzInvestments
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