Bắt đầu

Statistics

Growth DashboardThe Multi-Year Growth Dashboard provides a high-level snapshot of an asset’s historical performance directly on your chart. It calculates the total percentage growth for 1-year, 3-year, and 5-year periods based on exact calendar dates. Unlike simple bar-counting scripts, this indicator uses a "Time-Capsule" logic: - Calendar Precision: It calculates specific timestamps for 365, 1,095, and 1,825 days ago. - Persistent Memory: Using the var keyword, the script scans historical bars and "captures" the closing price as it crosses those specific dates. - Dividend Adjustment: It respects the chart's ADJ (Adjusted for Dividends) toggle, ensuring your total return figures are accurate for stocks like AAPL or MSFT.
Chỉ báo Pine Script®
của BigH57
Adaptive Market Wave TheoryAdaptive Market Wave Theory 🌊 CORE INNOVATION: PROBABILISTIC PHASE DETECTION WITH MULTI-AGENT CONSENSUS Adaptive Market Wave Theory (AMWT) represents a fundamental paradigm shift in how traders approach market phase identification. Rather than counting waves subjectively or drawing static breakout levels, AMWT treats the market as a hidden state machine —using Hidden Markov Models, multi-agent consensus systems, and reinforcement learning algorithms to quantify what traditional methods leave to interpretation. The Wave Analysis Problem: Traditional wave counting methodologies (Elliott Wave, harmonic patterns, ABC corrections) share fatal weaknesses that AMWT directly addresses: 1. Non-Falsifiability : Invalid wave counts can always be "recounted" or "adjusted." If your Wave 3 fails, it becomes "Wave 3 of a larger degree" or "actually Wave C." There's no objective failure condition. 2. Observer Bias : Two expert wave analysts examining the same chart routinely reach different conclusions. This isn't a feature—it's a fundamental methodology flaw. 3. No Confidence Measure : Traditional analysis says "This IS Wave 3." But with what probability? 51%? 95%? The binary nature prevents proper position sizing and risk management. 4. Static Rules : Fixed Fibonacci ratios and wave guidelines cannot adapt to changing market regimes. What worked in 2019 may fail in 2024. 5. No Accountability : Wave methodologies rarely track their own performance. There's no feedback loop to improve. The AMWT Solution: AMWT addresses each limitation through rigorous mathematical frameworks borrowed from speech recognition, machine learning, and reinforcement learning: • Non-Falsifiability → Hard Invalidation : Wave hypotheses die permanently when price violates calculated invalidation levels. No recounting allowed. • Observer Bias → Multi-Agent Consensus : Three independent analytical agents must agree. Single-methodology bias is eliminated. • No Confidence → Probabilistic States : Every market state has a calculated probability from Hidden Markov Model inference. "72% probability of impulse state" replaces "This is Wave 3." • Static Rules → Adaptive Learning : Thompson Sampling multi-armed bandits learn which agents perform best in current conditions. The system adapts in real-time. • No Accountability → Performance Tracking : Comprehensive statistics track every signal's outcome. The system knows its own performance. The Core Insight: "Traditional wave analysis asks 'What count is this?' AMWT asks 'What is the probability we are in an impulsive state, with what confidence, confirmed by how many independent methodologies, and anchored to what liquidity event?'" 🔬 THEORETICAL FOUNDATION: HIDDEN MARKOV MODELS Why Hidden Markov Models? Markets exist in hidden states that we cannot directly observe—only their effects on price are visible. When the market is in an "impulse up" state, we see rising prices, expanding volume, and trending indicators. But we don't observe the state itself—we infer it from observables. This is precisely the problem Hidden Markov Models (HMMs) solve. Originally developed for speech recognition (inferring words from sound waves), HMMs excel at estimating hidden states from noisy observations. HMM Components: 1. Hidden States (S) : The unobservable market conditions 2. Observations (O) : What we can measure (price, volume, indicators) 3. Transition Matrix (A) : Probability of moving between states 4. Emission Matrix (B) : Probability of observations given each state 5. Initial Distribution (π) : Starting state probabilities AMWT's Six Market States: State 0: IMPULSE_UP • Definition: Strong bullish momentum with high participation • Observable Signatures: Rising prices, expanding volume, RSI >60, price above upper Bollinger Band, MACD histogram positive and rising • Typical Duration: 5-20 bars depending on timeframe • What It Means: Institutional buying pressure, trend acceleration phase State 1: IMPULSE_DN • Definition: Strong bearish momentum with high participation • Observable Signatures: Falling prices, expanding volume, RSI <40, price below lower Bollinger Band, MACD histogram negative and falling • Typical Duration: 5-20 bars (often shorter than bullish impulses—markets fall faster) • What It Means: Institutional selling pressure, panic or distribution acceleration State 2: CORRECTION • Definition: Counter-trend consolidation with declining momentum • Observable Signatures: Sideways or mild counter-trend movement, contracting volume, RSI returning toward 50, Bollinger Bands narrowing • Typical Duration: 8-30 bars • What It Means: Profit-taking, digestion of prior move, potential accumulation for next leg State 3: ACCUMULATION • Definition: Base-building near lows where informed participants absorb supply • Observable Signatures: Price near recent lows but not making new lows, volume spikes on up bars, RSI showing positive divergence, tight range • Typical Duration: 15-50 bars • What It Means: Smart money buying from weak hands, preparing for markup phase State 4: DISTRIBUTION • Definition: Top-forming near highs where informed participants distribute holdings • Observable Signatures: Price near recent highs but struggling to advance, volume spikes on down bars, RSI showing negative divergence, widening range • Typical Duration: 15-50 bars • What It Means: Smart money selling to late buyers, preparing for markdown phase State 5: TRANSITION • Definition: Regime change period with mixed signals and elevated uncertainty • Observable Signatures: Conflicting indicators, whipsaw price action, no clear momentum, high volatility without direction • Typical Duration: 5-15 bars • What It Means: Market deciding next direction, dangerous for directional trades The Transition Matrix: The transition matrix A captures the probability of moving from one state to another. AMWT initializes with empirically-derived values then updates online: From/To IMP_UP IMP_DN CORR ACCUM DIST TRANS IMP_UP 0.70 0.02 0.20 0.02 0.04 0.02 IMP_DN 0.02 0.70 0.20 0.04 0.02 0.02 CORR 0.15 0.15 0.50 0.10 0.10 0.00 ACCUM 0.30 0.05 0.15 0.40 0.05 0.05 DIST 0.05 0.30 0.15 0.05 0.40 0.05 TRANS 0.20 0.20 0.20 0.15 0.15 0.10 Key Insights from Transition Probabilities: • Impulse states are sticky (70% self-transition): Once trending, markets tend to continue • Corrections can transition to either impulse direction (15% each): The next move after correction is uncertain • Accumulation strongly favors IMP_UP transition (30%): Base-building leads to rallies • Distribution strongly favors IMP_DN transition (30%): Topping leads to declines The Viterbi Algorithm: Given a sequence of observations, how do we find the most likely state sequence? This is the Viterbi algorithm—dynamic programming to find the optimal path through the state space. Mathematical Formulation: δ_t(j) = max_i × B_j(O_t) Where: δ_t(j) = probability of most likely path ending in state j at time t A_ij = transition probability from state i to state j B_j(O_t) = emission probability of observation O_t given state j AMWT Implementation: AMWT runs Viterbi over a rolling window (default 50 bars), computing the most likely state sequence and extracting: • Current state estimate • State confidence (probability of current state vs alternatives) • State sequence for pattern detection Online Learning (Baum-Welch Adaptation): Unlike static HMMs, AMWT continuously updates its transition and emission matrices based on observed market behavior: f_onlineUpdateHMM(prev_state, curr_state, observation, decay) => // Update transition matrix A *= decay A += (1.0 - decay) // Renormalize row // Update emission matrix B *= decay B += (1.0 - decay) // Renormalize row The decay parameter (default 0.85) controls adaptation speed: • Higher decay (0.95): Slower adaptation, more stable, better for consistent markets • Lower decay (0.80): Faster adaptation, more reactive, better for regime changes Why This Matters for Trading: Traditional indicators give you a number (RSI = 72). AMWT gives you a probabilistic state assessment : "There is a 78% probability we are in IMPULSE_UP state, with 15% probability of CORRECTION and 7% distributed among other states. The transition matrix suggests 70% chance of remaining in IMPULSE_UP next bar, 20% chance of transitioning to CORRECTION." This enables: • Position sizing by confidence : 90% confidence = full size; 60% confidence = half size • Risk management by transition probability : High correction probability = tighten stops • Strategy selection by state : IMPULSE = trend-follow; CORRECTION = wait; ACCUMULATION = scale in 🎰 THE 3-BANDIT CONSENSUS SYSTEM The Multi-Agent Philosophy: No single analytical methodology works in all market conditions. Trend-following excels in trending markets but gets chopped in ranges. Mean-reversion excels in ranges but gets crushed in trends. Structure-based analysis works when structure is clear but fails in chaotic markets. AMWT's solution: employ three independent agents , each analyzing the market from a different perspective, then use Thompson Sampling to learn which agents perform best in current conditions. Agent 1: TREND AGENT Philosophy : Markets trend. Follow the trend until it ends. Analytical Components: • EMA Alignment: EMA8 > EMA21 > EMA50 (bullish) or inverse (bearish) • MACD Histogram: Direction and rate of change • Price Momentum: Close relative to ATR-normalized movement • VWAP Position: Price above/below volume-weighted average price Signal Generation: Strong Bull: EMA aligned bull AND MACD histogram > 0 AND momentum > 0.3 AND close > VWAP → Signal: +1 (Long), Confidence: 0.75 + |momentum| × 0.4 Moderate Bull: EMA stack bull AND MACD rising AND momentum > 0.1 → Signal: +1 (Long), Confidence: 0.65 + |momentum| × 0.3 Strong Bear: EMA aligned bear AND MACD histogram < 0 AND momentum < -0.3 AND close < VWAP → Signal: -1 (Short), Confidence: 0.75 + |momentum| × 0.4 Moderate Bear: EMA stack bear AND MACD falling AND momentum < -0.1 → Signal: -1 (Short), Confidence: 0.65 + |momentum| × 0.3 When Trend Agent Excels: • Trend days (IB extension >1.5x) • Post-breakout continuation • Institutional accumulation/distribution phases When Trend Agent Fails: • Range-bound markets (ADX <20) • Chop zones after volatility spikes • Reversal days at major levels Agent 2: REVERSION AGENT Philosophy: Markets revert to mean. Extreme readings reverse. Analytical Components: • Bollinger Band Position: Distance from bands, percent B • RSI Extremes: Overbought (>70) and oversold (<30) • Stochastic: %K/%D crossovers at extremes • Band Squeeze: Bollinger Band width contraction Signal Generation: Oversold Bounce: BB %B < 0.20 AND RSI < 35 AND Stochastic < 25 → Signal: +1 (Long), Confidence: 0.70 + (30 - RSI) × 0.01 Overbought Fade: BB %B > 0.80 AND RSI > 65 AND Stochastic > 75 → Signal: -1 (Short), Confidence: 0.70 + (RSI - 70) × 0.01 Squeeze Fire Bull: Band squeeze ending AND close > upper band → Signal: +1 (Long), Confidence: 0.65 Squeeze Fire Bear: Band squeeze ending AND close < lower band → Signal: -1 (Short), Confidence: 0.65 When Reversion Agent Excels: • Rotation days (price stays within IB) • Range-bound consolidation • After extended moves without pullback When Reversion Agent Fails: • Strong trend days (RSI can stay overbought for days) • Breakout moves • News-driven directional moves Agent 3: STRUCTURE AGENT Philosophy: Market structure reveals institutional intent. Follow the smart money. Analytical Components: • Break of Structure (BOS): Price breaks prior swing high/low • Change of Character (CHOCH): First break against prevailing trend • Higher Highs/Higher Lows: Bullish structure • Lower Highs/Lower Lows: Bearish structure • Liquidity Sweeps: Stop runs that reverse Signal Generation: BOS Bull: Price breaks above prior swing high with momentum → Signal: +1 (Long), Confidence: 0.70 + structure_strength × 0.2 CHOCH Bull: First higher low after downtrend, breaking structure → Signal: +1 (Long), Confidence: 0.75 BOS Bear: Price breaks below prior swing low with momentum → Signal: -1 (Short), Confidence: 0.70 + structure_strength × 0.2 CHOCH Bear: First lower high after uptrend, breaking structure → Signal: -1 (Short), Confidence: 0.75 Liquidity Sweep Long: Price sweeps below swing low then reverses strongly → Signal: +1 (Long), Confidence: 0.80 Liquidity Sweep Short: Price sweeps above swing high then reverses strongly → Signal: -1 (Short), Confidence: 0.80 When Structure Agent Excels: • After liquidity grabs (stop runs) • At major swing points • During institutional accumulation/distribution When Structure Agent Fails: • Choppy, structureless markets • During news events (structure becomes noise) • Very low timeframes (noise overwhelms structure) Thompson Sampling: The Bandit Algorithm With three agents giving potentially different signals, how do we decide which to trust? This is the multi-armed bandit problem —balancing exploitation (using what works) with exploration (testing alternatives). Thompson Sampling Solution: Each agent maintains a Beta distribution representing its success/failure history: Agent success rate modeled as Beta(α, β) Where: α = number of successful signals + 1 β = number of failed signals + 1 On Each Bar: 1. Sample from each agent's Beta distribution 2. Weight agent signals by sampled probabilities 3. Combine weighted signals into consensus 4. Update α/β based on trade outcomes Mathematical Implementation: // Beta sampling via Gamma ratio method f_beta_sample(alpha, beta) => g1 = f_gamma_sample(alpha) g2 = f_gamma_sample(beta) g1 / (g1 + g2) // Thompson Sampling selection for each agent: sampled_prob = f_beta_sample(agent.alpha, agent.beta) weight = sampled_prob / sum(all_sampled_probs) consensus += agent.signal × agent.confidence × weight Why Thompson Sampling? • Automatic Exploration : Agents with few samples get occasional chances (high variance in Beta distribution) • Bayesian Optimal : Mathematically proven optimal solution to exploration-exploitation tradeoff • Uncertainty-Aware : Small sample size = more exploration; large sample size = more exploitation • Self-Correcting : Poor performers naturally get lower weights over time Example Evolution: Day 1 (Initial): Trend Agent: Beta(1,1) → samples ~0.50 (high uncertainty) Reversion Agent: Beta(1,1) → samples ~0.50 (high uncertainty) Structure Agent: Beta(1,1) → samples ~0.50 (high uncertainty) After 50 Signals: Trend Agent: Beta(28,23) → samples ~0.55 (moderate confidence) Reversion Agent: Beta(18,33) → samples ~0.35 (underperforming) Structure Agent: Beta(32,19) → samples ~0.63 (outperforming) Result: Structure Agent now receives highest weight in consensus Consensus Requirements by Mode: Aggressive Mode: • Minimum 1/3 agents agreeing • Consensus threshold: 45% • Use case: More signals, higher risk tolerance Balanced Mode: • Minimum 2/3 agents agreeing • Consensus threshold: 55% • Use case: Standard trading Conservative Mode: • Minimum 2/3 agents agreeing • Consensus threshold: 65% • Use case: Higher quality, fewer signals Institutional Mode: • Minimum 2/3 agents agreeing • Consensus threshold: 75% • Additional: Session quality >0.65, mode adjustment +0.10 • Use case: Highest quality signals only 🌀 INTELLIGENT CHOP DETECTION ENGINE The Chop Problem: Most trading losses occur not from being wrong about direction, but from trading in conditions where direction doesn't exist . Choppy, range-bound markets generate false signals from every methodology—trend-following, mean-reversion, and structure-based alike. AMWT's chop detection engine identifies these low-probability environments before signals fire, preventing the most damaging trades. Five-Factor Chop Analysis: Factor 1: ADX Component (25% weight) ADX (Average Directional Index) measures trend strength regardless of direction. ADX < 15: Very weak trend (high chop score) ADX 15-20: Weak trend (moderate chop score) ADX 20-25: Developing trend (low chop score) ADX > 25: Strong trend (minimal chop score) adx_chop = (i_adxThreshold - adx_val) / i_adxThreshold × 100 Why ADX Works: ADX synthesizes +DI and -DI movements. Low ADX means price is moving but not directionally—the definition of chop. Factor 2: Choppiness Index (25% weight) The Choppiness Index measures price efficiency using the ratio of ATR sum to price range: CI = 100 × LOG10(SUM(ATR, n) / (Highest - Lowest)) / LOG10(n) CI > 61.8: Choppy (range-bound, inefficient movement) CI < 38.2: Trending (directional, efficient movement) CI 38.2-61.8: Transitional chop_idx_score = (ci_val - 38.2) / (61.8 - 38.2) × 100 Why Choppiness Index Works: In trending markets, price covers distance efficiently (low ATR sum relative to range). In choppy markets, price oscillates wildly but goes nowhere (high ATR sum relative to range). Factor 3: Range Compression (20% weight) Compares recent range to longer-term range, detecting volatility squeezes: recent_range = Highest(20) - Lowest(20) longer_range = Highest(50) - Lowest(50) compression = 1 - (recent_range / longer_range) compression > 0.5: Strong squeeze (potential breakout imminent) compression < 0.2: No compression (normal volatility) range_compression_score = compression × 100 Why Range Compression Matters: Compression precedes expansion. High compression = market coiling, preparing for move. Signals during compression often fail because the breakout hasn't occurred yet. Factor 4: Channel Position (15% weight) Tracks price position within the macro channel: channel_position = (close - channel_low) / (channel_high - channel_low) position 0.4-0.6: Center of channel (indecision zone) position <0.2 or >0.8: Near extremes (potential reversal or breakout) channel_chop = abs(0.5 - channel_position) < 0.15 ? high_score : low_score Why Channel Position Matters: Price in the middle of a range is in "no man's land"—equally likely to go either direction. Signals in the channel center have lower probability. Factor 5: Volume Quality (15% weight) Assesses volume relative to average: vol_ratio = volume / SMA(volume, 20) vol_ratio < 0.7: Low volume (lack of conviction) vol_ratio 0.7-1.3: Normal volume vol_ratio > 1.3: High volume (conviction present) volume_chop = vol_ratio < 0.8 ? (1 - vol_ratio) × 100 : 0 Why Volume Quality Matters: Low volume moves lack institutional participation. These moves are more likely to reverse or stall. Combined Chop Intensity: chopIntensity = (adx_chop × 0.25) + (chop_idx_score × 0.25) + (range_compression_score × 0.20) + (channel_chop × 0.15) + (volume_chop × i_volumeChopWeight × 0.15) Regime Classifications: Based on chop intensity and component analysis: • Strong Trend (0-20%): ADX >30, clear directional momentum, trade aggressively • Trending (20-35%): ADX >20, moderate directional bias, trade normally • Transitioning (35-50%): Mixed signals, regime change possible, reduce size • Mid-Range (50-60%): Price trapped in channel center, avoid new positions • Ranging (60-70%): Low ADX, price oscillating within bounds, fade extremes only • Compression (70-80%): Volatility squeeze, expansion imminent, wait for breakout • Strong Chop (80-100%): Multiple chop factors aligned, avoid trading entirely Signal Suppression: When chop intensity exceeds the configurable threshold (default 80%), signals are suppressed entirely. The dashboard displays "⚠️ CHOP ZONE" with the current regime classification. Chop Box Visualization: When chop is detected, AMWT draws a semi-transparent box on the chart showing the chop zone. This visual reminder helps traders avoid entering positions during unfavorable conditions. 💧 LIQUIDITY ANCHORING SYSTEM The Liquidity Concept: Markets move from liquidity pool to liquidity pool. Stop losses cluster at predictable locations—below swing lows (buy stops become sell orders when triggered) and above swing highs (sell stops become buy orders when triggered). Institutions know where these clusters are and often engineer moves to trigger them before reversing. AMWT identifies and tracks these liquidity events, using them as anchors for signal confidence. Liquidity Event Types: Type 1: Volume Spikes Definition: Volume > SMA(volume, 20) × i_volThreshold (default 2.8x) Interpretation: Sudden volume surge indicates institutional activity • Near swing low + reversal: Likely accumulation • Near swing high + reversal: Likely distribution • With continuation: Institutional conviction in direction Type 2: Stop Runs (Liquidity Sweeps) Definition: Price briefly exceeds swing high/low then reverses within N bars Detection: • Price breaks above recent swing high (triggering buy stops) • Then closes back below that high within 3 bars • Signal: Bullish stop run complete, reversal likely Or inverse for bearish: • Price breaks below recent swing low (triggering sell stops) • Then closes back above that low within 3 bars • Signal: Bearish stop run complete, reversal likely Type 3: Absorption Events Definition: High volume with small candle body Detection: • Volume > 2x average • Candle body < 30% of candle range • Interpretation: Large orders being filled without moving price • Implication: Accumulation (at lows) or distribution (at highs) Type 4: BSL/SSL Pools (Buy-Side/Sell-Side Liquidity) BSL (Buy-Side Liquidity): • Cluster of swing highs within ATR proximity • Stop losses from shorts sit above these highs • Breaking BSL triggers short covering (fuel for rally) SSL (Sell-Side Liquidity): • Cluster of swing lows within ATR proximity • Stop losses from longs sit below these lows • Breaking SSL triggers long liquidation (fuel for decline) Liquidity Pool Mapping: AMWT continuously scans for and maps liquidity pools: // Detect swing highs/lows using pivot function swing_high = ta.pivothigh(high, 5, 5) swing_low = ta.pivotlow(low, 5, 5) // Track recent swing points if not na(swing_high) bsl_levels.push(swing_high) if not na(swing_low) ssl_levels.push(swing_low) // Display on chart with labels Confluence Scoring Integration: When signals fire near identified liquidity events, confluence scoring increases: • Signal near volume spike: +10% confidence • Signal after liquidity sweep: +15% confidence • Signal at BSL/SSL pool: +10% confidence • Signal aligned with absorption zone: +10% confidence Why Liquidity Anchoring Matters: Signals "in a vacuum" have lower probability than signals anchored to institutional activity. A long signal after a liquidity sweep below swing lows has trapped shorts providing fuel. A long signal in the middle of nowhere has no such catalyst. 📊 SIGNAL GRADING SYSTEM The Quality Problem: Not all signals are created equal. A signal with 6/6 factors aligned is fundamentally different from a signal with 3/6 factors aligned. Traditional indicators treat them the same. AMWT grades every signal based on confluence. Confluence Components (100 points total): 1. Bandit Consensus Strength (25 points) consensus_str = weighted average of agent confidences score = consensus_str × 25 Example: Trend Agent: +1 signal, 0.80 confidence, 0.35 weight Reversion Agent: 0 signal, 0.50 confidence, 0.25 weight Structure Agent: +1 signal, 0.75 confidence, 0.40 weight Weighted consensus = (0.80×0.35 + 0×0.25 + 0.75×0.40) / (0.35 + 0.40) = 0.77 Score = 0.77 × 25 = 19.25 points 2. HMM State Confidence (15 points) score = hmm_confidence × 15 Example: HMM reports 82% probability of IMPULSE_UP Score = 0.82 × 15 = 12.3 points 3. Session Quality (15 points) Session quality varies by time: • London/NY Overlap: 1.0 (15 points) • New York Session: 0.95 (14.25 points) • London Session: 0.70 (10.5 points) • Asian Session: 0.40 (6 points) • Off-Hours: 0.30 (4.5 points) • Weekend: 0.10 (1.5 points) 4. Energy/Participation (10 points) energy = (realized_vol / avg_vol) × 0.4 + (range / ATR) × 0.35 + (volume / avg_volume) × 0.25 score = min(energy, 1.0) × 10 5. Volume Confirmation (10 points) if volume > SMA(volume, 20) × 1.5: score = 10 else if volume > SMA(volume, 20): score = 5 else: score = 0 6. Structure Alignment (10 points) For long signals: • Bullish structure (HH + HL): 10 points • Higher low only: 6 points • Neutral structure: 3 points • Bearish structure: 0 points Inverse for short signals 7. Trend Alignment (10 points) For long signals: • Price > EMA21 > EMA50: 10 points • Price > EMA21: 6 points • Neutral: 3 points • Against trend: 0 points 8. Entry Trigger Quality (5 points) • Strong trigger (multiple confirmations): 5 points • Moderate trigger (single confirmation): 3 points • Weak trigger (marginal): 1 point Grade Scale: Total Score → Grade 85-100 → A+ (Exceptional—all factors aligned) 70-84 → A (Strong—high probability) 55-69 → B (Acceptable—proceed with caution) Below 55 → C (Marginal—filtered by default) Grade-Based Signal Brightness: Signal arrows on the chart have transparency based on grade: • A+: Full brightness (alpha = 0) • A: Slight fade (alpha = 15) • B: Moderate fade (alpha = 35) • C: Significant fade (alpha = 55) This visual hierarchy helps traders instantly identify signal quality. Minimum Grade Filter: Configurable filter (default: C) sets the minimum grade for signal display: • Set to "A" for only highest-quality signals • Set to "B" for moderate selectivity • Set to "C" for all signals (maximum quantity) 🕐 SESSION INTELLIGENCE Why Sessions Matter: Markets behave differently at different times. The London open is fundamentally different from the Asian lunch hour. AMWT incorporates session-aware logic to optimize signal quality. Session Definitions: Asian Session (18:00-03:00 ET) • Characteristics: Lower volatility, range-bound tendency, fewer institutional participants • Quality Score: 0.40 (40% of peak quality) • Strategy Implications: Fade extremes, expect ranges, smaller position sizes • Best For: Mean-reversion setups, accumulation/distribution identification London Session (03:00-12:00 ET) • Characteristics: European institutional activity, volatility pickup, trend initiation • Quality Score: 0.70 (70% of peak quality) • Strategy Implications: Watch for trend development, breakouts more reliable • Best For: Initial trend identification, structure breaks New York Session (08:00-17:00 ET) • Characteristics: Highest liquidity, US institutional activity, major moves • Quality Score: 0.95 (95% of peak quality) • Strategy Implications: Best environment for directional trades • Best For: Trend continuation, momentum plays London/NY Overlap (08:00-12:00 ET) • Characteristics: Peak liquidity, both European and US participants active • Quality Score: 1.0 (100%—maximum quality) • Strategy Implications: Highest probability for successful breakouts and trends • Best For: All signal types—this is prime time Off-Hours • Characteristics: Thin liquidity, erratic price action, gaps possible • Quality Score: 0.30 (30% of peak quality) • Strategy Implications: Avoid new positions, wider stops if holding • Best For: Waiting Smart Weekend Detection: AMWT properly handles the Sunday evening futures open: // Traditional (broken): isWeekend = dayofweek == saturday OR dayofweek == sunday // AMWT (correct): anySessionActive = not na(asianTime) or not na(londonTime) or not na(nyTime) isWeekend = calendarWeekend AND NOT anySessionActive This ensures Sunday 6pm ET (when futures open) correctly shows "Asian Session" rather than "Weekend." Session Transition Boosts: Certain session transitions create trading opportunities: • Asian → London transition: +15% confidence boost (volatility expansion likely) • London → Overlap transition: +20% confidence boost (peak liquidity approaching) • Overlap → NY-only transition: -10% confidence adjustment (liquidity declining) • Any → Off-Hours transition: Signal suppression recommended 📈 TRADE MANAGEMENT SYSTEM The Signal Spam Problem: Many indicators generate signal after signal, creating confusion and overtrading. AMWT implements a complete trade lifecycle management system that prevents signal spam and tracks performance. Trade Lock Mechanism: Once a signal fires, the system enters a "trade lock" state: Trade Lock Duration: Configurable (default 30 bars) Early Exit Conditions: • TP3 hit (full target reached) • Stop Loss hit (trade failed) • Lock expiration (time-based exit) During lock: • No new signals of same type displayed • Opposite signals can override (reversal) • Trade status tracked in dashboard Target Levels: Each signal generates three profit targets based on ATR: TP1 (Conservative Target) • Default: 1.0 × ATR • Purpose: Quick partial profit, reduce risk • Action: Take 30-40% off position, move stop to breakeven TP2 (Standard Target) • Default: 2.5 × ATR • Purpose: Main profit target • Action: Take 40-50% off position, trail stop TP3 (Extended Target) • Default: 5.0 × ATR • Purpose: Runner target for trend days • Action: Close remaining position or continue trailing Stop Loss: • Default: 1.9 × ATR from entry • Purpose: Define maximum risk • Placement: Below recent swing low (longs) or above recent swing high (shorts) Invalidation Level: Beyond stop loss, AMWT calculates an "invalidation" level where the wave hypothesis dies: invalidation = entry - (ATR × INVALIDATION_MULT × 1.5) If price reaches invalidation, the current market interpretation is wrong—not just the trade. Visual Trade Management: During active trades, AMWT displays: • Entry arrow with grade label (▲A+, ▼B, etc.) • TP1, TP2, TP3 horizontal lines in green • Stop Loss line in red • Invalidation line in orange (dashed) • Progress indicator in dashboard Persistent Execution Markers: When targets or stops are hit, permanent markers appear: • TP hit: Green dot with "TP1"/"TP2"/"TP3" label • SL hit: Red dot with "SL" label These persist on the chart for review and statistics. 💰 PERFORMANCE TRACKING & STATISTICS Tracked Metrics: • Total Trades: Count of all signals that entered trade lock • Winning Trades: Signals where at least TP1 was reached before SL • Losing Trades: Signals where SL was hit before any TP • Win Rate: Winning / Total × 100% • Total R Profit: Sum of R-multiples from winning trades • Total R Loss: Sum of R-multiples from losing trades • Net R: Total R Profit - Total R Loss Currency Conversion System: AMWT can display P&L in multiple formats: R-Multiple (Default) • Shows risk-normalized returns • "Net P&L: +4.2R | 78 trades" means 4.2 times initial risk gained over 78 trades • Best for comparing across different position sizes Currency Conversion (USD/EUR/GBP/JPY/INR) • Converts R-multiples to currency based on: - Dollar Risk Per Trade (user input) - Tick Value (user input) - Selected currency Example Configuration: Dollar Risk Per Trade: $100 Display Currency: USD If Net R = +4.2R Display: Net P&L: +$420.00 | 78 trades Ticks • For futures traders who think in ticks • Converts based on tick value input Statistics Reset: Two reset methods: 1. Toggle Reset • Turn "Reset Statistics" toggle ON then OFF • Clears all statistics immediately 2. Date-Based Reset • Set "Reset After Date" (YYYY-MM-DD format) • Only trades after this date are counted • Useful for isolating recent performance 🎨 VISUAL FEATURES Macro Channel: Dynamic regression-based channel showing market boundaries: • Upper/lower bounds calculated from swing pivot linear regression • Adapts to current market structure • Shows overall trend direction and potential reversal zones Chop Boxes: Semi-transparent overlay during high-chop periods: • Purple/orange coloring indicates dangerous conditions • Visual reminder to avoid new positions Confluence Heat Zones: Background shading indicating setup quality: • Darker shading = higher confluence • Lighter shading = lower confluence • Helps identify optimal entry timing EMA Ribbon: Trend visualization via moving average fill: • EMA 8/21/50 with gradient fill between • Green fill when bullish aligned • Red fill when bearish aligned • Gray when neutral Absorption Zone Boxes: Marks potential accumulation/distribution areas: • High volume + small body = absorption • Boxes drawn at these levels • Often act as support/resistance Liquidity Pool Lines: BSL/SSL levels with labels: • Dashed lines at liquidity clusters • "BSL" label above swing high clusters • "SSL" label below swing low clusters Six Professional Themes: • Quantum: Deep purples and cyans (default) • Cyberpunk: Neon pinks and blues • Professional: Muted grays and greens • Ocean: Blues and teals • Matrix: Greens and blacks • Ember: Oranges and reds 🎓 PROFESSIONAL USAGE PROTOCOL Phase 1: Learning the System (Week 1) Goal: Understand AMWT concepts and dashboard interpretation Setup: • Signal Mode: Balanced • Display: All features enabled • Grade Filter: C (see all signals) Actions: • Paper trade ONLY—no real money • Observe HMM state transitions throughout the day • Note when agents agree vs disagree • Watch chop detection engage and disengage • Track which grades produce winners vs losers Key Learning Questions: • How often do A+ signals win vs B signals? (Should see clear difference) • Which agent tends to be right in current market? (Check dashboard) • When does chop detection save you from bad trades? • How do signals near liquidity events perform vs signals in vacuum? Phase 2: Parameter Optimization (Week 2) Goal: Tune system to your instrument and timeframe Signal Mode Testing: • Run 5 days on Aggressive mode (more signals) • Run 5 days on Conservative mode (fewer signals) • Compare: Which produces better risk-adjusted returns? Grade Filter Testing: • Track A+ only for 20 signals • Track A and above for 20 signals • Track B and above for 20 signals • Compare win rates and expectancy Chop Threshold Testing: • Default (80%): Standard filtering • Try 70%: More aggressive filtering • Try 90%: Less filtering • Which produces best results for your instrument? Phase 3: Strategy Development (Weeks 3-4) Goal: Develop personal trading rules based on system signals Position Sizing by Grade: • A+ grade: 100% position size • A grade: 75% position size • B grade: 50% position size • C grade: 25% position size (or skip) Session-Based Rules: • London/NY Overlap: Take all A/A+ signals • NY Session: Take all A+ signals, selective on A • Asian Session: Only A+ signals with extra confirmation • Off-Hours: No new positions Chop Zone Rules: • Chop >70%: Reduce position size 50% • Chop >80%: No new positions • Chop <50%: Full position size allowed Phase 4: Live Micro-Sizing (Month 2) Goal: Validate paper trading results with minimal risk Setup: • 10-20% of intended full position size • Take ONLY A+ signals initially • Follow trade management religiously Tracking: • Log every trade: Entry, Exit, Grade, HMM State, Chop Level, Agent Consensus • Calculate: Win rate by grade, by session, by chop level • Compare to paper trading (should be within 15%) Red Flags: • Win rate diverges significantly from paper trading: Execution issues • Consistent losses during certain sessions: Adjust session rules • Losses cluster when specific agent dominates: Review that agent's logic Phase 5: Scaling Up (Months 3-6) Goal: Gradually increase to full position size Progression: • Month 3: 25-40% size (if micro-sizing profitable) • Month 4: 40-60% size • Month 5: 60-80% size • Month 6: 80-100% size Scale-Up Requirements: • Minimum 30 trades at current size • Win rate ≥50% • Net R positive • No revenge trading incidents • Emotional control maintained 💡 DEVELOPMENT INSIGHTS Why HMM Over Simple Indicators: Early versions used standard indicators (RSI >70 = overbought, etc.). Win rates hovered at 52-55%. The problem: indicators don't capture state. RSI can stay "overbought" for weeks in a strong trend. The insight: markets exist in states, and state persistence matters more than indicator levels. Implementing HMM with state transition probabilities increased signal quality significantly. The system now knows not just "RSI is high" but "we're in IMPULSE_UP state with 70% probability of staying in IMPULSE_UP." The Multi-Agent Evolution: Original version used a single analytical methodology—trend-following. Performance was inconsistent: great in trends, destroyed in ranges. Added mean-reversion agent: now it was inconsistent the other way. The breakthrough: use multiple agents and let the system learn which works . Thompson Sampling wasn't the first attempt—tried simple averaging, voting, even hard-coded regime switching. Thompson Sampling won because it's mathematically optimal and automatically adapts without manual regime detection. Chop Detection Revelation: Chop detection was added almost as an afterthought. "Let's filter out obviously bad conditions." Testing revealed it was the most impactful single feature. Filtering chop zones reduced losing trades by 35% while only reducing total signals by 20%. The insight: avoiding bad trades matters more than finding good ones. Liquidity Anchoring Discovery: Watched hundreds of trades. Noticed pattern: signals that fired after liquidity events (stop runs, volume spikes) had significantly higher win rates than signals in quiet markets. Implemented liquidity detection and anchoring. Win rate on liquidity-anchored signals: 68% vs 52% on non-anchored signals. The Grade System Impact: Early system had binary signals (fire or don't fire). Adding grading transformed it. Traders could finally match position size to signal quality. A+ signals deserved full size; C signals deserved caution. Just implementing grade-based sizing improved portfolio Sharpe ratio by 0.3. 🚨 LIMITATIONS & CRITICAL ASSUMPTIONS What AMWT Is NOT: • NOT a Holy Grail : No system wins every trade. AMWT improves probability, not certainty. • NOT Fully Automated : AMWT provides signals and analysis; execution requires human judgment. • NOT News-Proof : Exogenous shocks (FOMC surprises, geopolitical events) invalidate all technical analysis. • NOT for Scalping : HMM state estimation needs time to develop. Sub-minute timeframes are not appropriate. Core Assumptions: 1. Markets Have States : Assumes markets transition between identifiable regimes. Violation: Random walk markets with no regime structure. 2. States Are Inferable : Assumes observable indicators reveal hidden states. Violation: Market manipulation creating false signals. 3. History Informs Future : Assumes past agent performance predicts future performance. Violation: Regime changes that invalidate historical patterns. 4. Liquidity Events Matter : Assumes institutional activity creates predictable patterns. Violation: Markets with no institutional participation. Performs Best On: • Liquid Futures : ES, NQ, MNQ, MES, CL, GC • Major Forex Pairs : EUR/USD, GBP/USD, USD/JPY • Large-Cap Stocks : AAPL, MSFT, TSLA, NVDA (>$5B market cap) • Liquid Crypto : BTC, ETH on major exchanges Performs Poorly On: • Illiquid Instruments : Low volume stocks, exotic pairs • Very Low Timeframes : Sub-5-minute charts (noise overwhelms signal) • Binary Event Days : Earnings, FDA approvals, court rulings • Manipulated Markets : Penny stocks, low-cap altcoins Known Weaknesses: • Warmup Period : HMM needs ~50 bars to initialize properly. Early signals may be unreliable. • Regime Change Lag : Thompson Sampling adapts over time, not instantly. Sudden regime changes may cause short-term underperformance. • Complexity : More parameters than simple indicators. Requires understanding to use effectively. ⚠️ RISK DISCLOSURE Trading futures, stocks, options, forex, and cryptocurrencies involves substantial risk of loss and is not suitable for all investors. Adaptive Market Wave Theory, while based on rigorous mathematical frameworks including Hidden Markov Models and multi-armed bandit algorithms, does not guarantee profits and can result in significant losses. AMWT's methodologies—HMM state estimation, Thompson Sampling agent selection, and confluence-based grading—have theoretical foundations but past performance is not indicative of future results. Hidden Markov Model assumptions may not hold during: • Major news events disrupting normal market behavior • Flash crashes or circuit breaker events • Low liquidity periods with erratic price action • Algorithmic manipulation or spoofing Multi-agent consensus assumes independent analytical perspectives provide edge. Market conditions change. Edges that existed historically can diminish or disappear. Users must independently validate system performance on their specific instruments, timeframes, and broker execution environment. Paper trade extensively before risking capital. Start with micro position sizing. Never risk more than you can afford to lose completely. Use proper position sizing. Implement stop losses without exception. By using this indicator, you acknowledge these risks and accept full responsibility for all trading decisions and outcomes. "Elliott Wave was a first-order approximation of market phase behavior. AMWT is the second—probabilistic, adaptive, and accountable." Initial Public Release Core Engine: • True Hidden Markov Model with online Baum-Welch learning • Viterbi algorithm for optimal state sequence decoding • 6-state market regime classification Agent System: • 3-Bandit consensus (Trend, Reversion, Structure) • Thompson Sampling with true Beta distribution sampling • Adaptive weight learning based on performance Signal Generation: • Quality-based confluence grading (A+/A/B/C) • Four signal modes (Aggressive/Balanced/Conservative/Institutional) • Grade-based visual brightness Chop Detection: • 5-factor analysis (ADX, Choppiness Index, Range Compression, Channel Position, Volume) • 7 regime classifications • Configurable signal suppression threshold Liquidity: • Volume spike detection • Stop run (liquidity sweep) identification • BSL/SSL pool mapping • Absorption zone detection Trade Management: • Trade lock with configurable duration • TP1/TP2/TP3 targets • ATR-based stop loss • Persistent execution markers Session Intelligence: • Asian/London/NY/Overlap detection • Smart weekend handling (Sunday futures open) • Session quality scoring Performance: • Statistics tracking with reset functionality • 7 currency display modes • Win rate and Net R calculation Visuals: • Macro channel with linear regression • Chop boxes • EMA ribbon • Liquidity pool lines • 6 professional themes Dashboards: • Main Dashboard: Market State, Consensus, Trade Status, Statistics 📋 AMWT vs AMWT-PRO: This version includes all core AMWT functionality: ✓ Full Hidden Markov Model state estimation ✓ 3-Bandit Thompson Sampling consensus system ✓ Complete 5-factor chop detection engine ✓ All four signal modes ✓ Full trade management with TP/SL tracking ✓ Main dashboard with complete statistics ✓ All visual features (channels, zones, pools) ✓ Identical signal generation to PRO ✓ Six professional themes ✓ Full alert system The PRO version adds the AMWT Advisor panel—a secondary dashboard providing: • Real-time Market Pulse situation assessment • Agent Matrix visualization (individual agent votes) • Structure analysis breakdown • "Watch For" upcoming setups • Action Command coaching Both versions generate identical signals . The Advisor provides additional guidance for interpreting those signals. Taking you to school. - Dskyz, Trade with probability. Trade with consensus. Trade with AMWT.
Chỉ báo Pine Script®
của DskyzInvestments
2
USDT: Market cap changeUSDT: Market Cap Change This indicator tracks the market capitalization changes of major stablecoins (USDT, USDC, and DAI) to help identify capital flows in the cryptocurrency market. Features: Monitor daily and custom period market cap changes for selected stablecoins Configurable stablecoin selection (USDT, USDC, DAI) Adjustable lookback period for measuring market cap changes Multiple moving average types (SMA, EMA, HMA, WMA, RMA) for trend analysis Visual representation with columns for daily changes and area fill for custom period changes How to Use: The indicator displays two main metrics: daily market cap change (shown as columns) and custom period change (shown as a line with area fill). Positive values indicate capital inflow into stablecoins, which may suggest accumulation or risk-off sentiment. Negative values indicate capital outflow, potentially signaling deployment into other crypto assets. The moving average overlay helps identify trends in stablecoin market cap changes over time. Settings: Select which stablecoins to track Adjust the lookback period (default: 60 days) Toggle and configure the moving average overlay Customize MA type and length Data Source: Uses Glassnode market capitalization data for USDT, USDC, and DAI on a daily timeframe.
Chỉ báo Pine Script®
của qowazzz
US Index Market Snapshot Cash, Futures & ETFsBrief Description This study displays a real-time table of major U.S. equity indices—Dow Jones, S&P 500, Nasdaq, and Russell—across Cash, Futures, and ETF markets. Each cell shows the current price along with the daily percentage change, with color-coded backgrounds for quick trend identification. Designed as a compact market dashboard, it provides an at-a-glance view of cross-market alignment and relative performance. Alternative Title Options US Indices Dashboard (Cash • Futures • ETFs) Index Market Matrix – Prices & Daily Change Multi-Market US Index Table
Chỉ báo Pine Script®
của frankenx
FVG & OB [odnac]This indicator is a sophisticated tool designed for Smart Money Concepts (SMC) traders. It automates the detection of two critical institutional footprints: Order Blocks (OB) and Fair Value Gaps (FVG), with a focus on candle momentum and mitigation tracking. Key Features 1. Advanced Momentum Filtering (3 Versions) Unlike basic indicators, this script uses three different mathematical approaches to ensure the middle candle represents a "strong" move: V1 (Body Focus): Compares the bodies of the surrounding candles to the middle candle. V2 (Hybrid): Uses a mix of candle ranges and bodies to identify expansion. V3 (Range Focus): The most aggressive filter; it ensures the total range of the middle candle dwarfs the surrounding candles. 2. Automatic Mitigation Tracking The indicator doesn't just draw static boxes. It tracks price action in real-time: Dynamic Extension: Boxes extend to the right automatically as long as price has not returned to "test" or "fill" the zone. Smart Clean-up: Once the price touches the zone (Mitigation), the box stops extending or is removed. This keeps your chart clean and focused only on "fresh" (unmitigated) levels. 3. Smart Money Concept Integration Order Blocks (White Boxes): Identifies where institutional buying or selling occurred before a strong move. Fair Value Gaps (Yellow Boxes): Highlights price imbalances where the market moved too fast, leaving a gap that often acts as a magnet for future price action. Technical Logic Breakdown Detection Logic The script looks at a 3-candle sequence: Candle (The Origin): Defines the boundary of the OB or FVG. Candle (The Expansion): Must be a "Strong Candle" based on your selected setting (V1, V2, or V3). Candle (The Confirmation): Ensures that the "Tail Gap" condition is met (the wick of Candle 2 and Candle 0 do not touch). Box Management The script uses Pine Script Arrays to manage up to 500 boxes. It constantly loops through active boxes to check: Time Limit: If a box exceeds the max_bars_extend limit, it is removed to save memory. Price Touch: If low or high enters the box coordinates, the zone is considered "mitigated" and the extension stops.
Chỉ báo Pine Script®
của odnac
Asia & London Session High/Low Description: This indicator plots the highest and lowest points of the Asian and London trading sessions based on Eastern Time (ET). Features: Draws horizontal rays for session highs and lows Automatically resets for each session Perfect for I CT-style liquidity analysis , range breaks , and session-based trading setups Clean chart : no labels or clutter, just the key session levels Use it to identify liquidity zones , plan entries , and anticipate potential session raids in your trading strategy.
Chỉ báo Pine Script®
của ggeorgiev9pm
Liquidation Map [Alpha Extract]A sophisticated liquidity distribution visualization system that identifies potential liquidation zones through pivot-based detection and renders them as an interactive histogram with cumulative distance-to-liquidation curves. Utilizing multi-exchange volume aggregation and ATR-scaled pocket detection, this indicator delivers institutional-grade liquidity mapping with real-time histogram display showing relative concentration of long and short liquidation levels across configurable price ranges. The system's box-based rendering architecture combined with cumulative distribution overlays provides comprehensive visual assessment of asymmetric liquidity positioning for strategic trade planning. 🔶 Advanced Multi-Exchange Aggregation Framework Implements intelligent ticker detection and multi-source volume aggregation across major exchanges including Binance, Bybit, KuCoin, OKX, and MEXC for accurate liquidity weight calculations. The system automatically identifies base currency (BTC, ETH, SOL) from chart ticker, retrieves volume data from matching perpetual contracts across multiple venues, and aggregates into composite volume metric for enhanced pocket weighting accuracy. 🔶 Pivot-Based Liquidation Pocket Detection Features sophisticated swing point identification using configurable pivot width with ATR-scaled vertical zone construction for volatility-adaptive pocket sizing. The system detects pivot highs for short liquidation zones (placed above swing) and pivot lows for long liquidation zones (placed below swing), applying 200-period ATR with percentage multipliers to determine pocket heights that adjust to market volatility conditions. 🔶 Interactive Histogram Visualization Engine Provides real-time box-based histogram rendering in indicator pane with configurable bin counts (up to 400 columns) and adjustable height, displaying liquidity concentration across fixed percentage range above and below current price. The system calculates bin sizes from view range, accumulates pocket weights into price bins, and renders vertical bars with gradient color intensity reflecting relative liquidity concentration at each price level. 🔶 Cumulative Distance Overlay System Implements innovative cumulative distribution curves showing aggregate liquidity distance from current price for both long (left) and short (right) positions. The system calculates running totals of pocket weights from current price outward in both directions, normalizes against maximum span, and overlays line segments showing how much total liquidity exists at various distances, enabling instant assessment of liquidation cascade potential. 🔶 Dynamic Price Range Adaptation Features fixed percentage-based view window that maintains consistent price range visualization across all timeframes and instruments, automatically centering histogram on current price with configurable +/- percentage bounds. The system recalculates histogram bins and pocket distributions on each bar close, ensuring visualization adapts to price movement while maintaining interpretable scale regardless of volatility regime. 🔶 Touch Detection and Weight Adjustment Provides intelligent pocket state tracking that identifies when price trades through liquidation zones and applies configurable weight multipliers to touched pockets for historical context. The system monitors price interaction with pocket midpoints, marks pockets as "hit" when violated, and optionally increases their visual weight (default 5x) to emphasize historical liquidation levels while distinguishing from untouched future zones. 🔶 Gradient Intensity Color System Implements sophisticated color gradient engine that modulates bar opacity from transparent to opaque based on relative liquidity concentration within each bin. The system normalizes bin values against maximum liquidity, applies color interpolation from faded to vivid hues, and distinguishes long liquidation zones (cyan) from short liquidation zones (yellow/gold) with current price column highlighted in red for instant orientation. 🔶 Performance-Optimized Rendering Architecture Utilizes efficient box and line object management with dynamic allocation based on histogram configuration, implementing intelligent cleanup and reuse to maintain smooth performance. The system includes adaptive line budget calculations that adjust segment density for cumulative curves based on available object limits, ensuring consistent operation even with maximum histogram resolution settings. 🔶 Asymmetric Distribution Analysis Calculates separate cumulative distributions for long and short liquidation zones split at current price, enabling identification of imbalanced liquidity positioning. The system normalizes distributions against respective maximums and overlays both curves on single histogram, allowing traders to instantly assess whether more liquidation risk exists above (shorts vulnerable) or below (longs vulnerable) current price levels. 🔶 Configurable Label and Scale System Provides price axis labeling with adjustable frequency to reduce clutter while maintaining reference points, displaying price values at regular column intervals with configurable offset positioning. The system includes current price label showing exact value and percentile position within view range, offering both absolute price reference and relative positioning context for distribution interpretation. 🔶 Historical Pocket Persistence Framework Maintains rolling window of liquidation pockets up to 3000 bars with automatic expiration management and optional preservation of touched zones for historical analysis. The system tracks pocket creation time, monitors age against lookback limits, and manages array cleanup to prevent memory overflow while retaining relevant historical liquidation levels for pattern recognition and support/resistance validation. This indicator delivers sophisticated liquidity distribution analysis through histogram visualization and cumulative distance curves that reveal asymmetric positioning of potential liquidation levels. Unlike simple liquidation heatmaps that show absolute levels, the Liquidation Map's cumulative distribution overlays instantly communicate how much total liquidity exists at various distances from current price, enabling assessment of cascade potential. The system's multi-exchange volume aggregation, touch-weighted historical zones, and fixed-range visualization make it essential for traders seeking strategic positioning around institutional liquidity clusters in cryptocurrency futures markets. The histogram format enables instant identification of price levels where concentrated liquidations may trigger significant volatility or reversal events, while the asymmetric distribution curves reveal whether market structure favors upside or downside cascades.
Chỉ báo Pine Script®
của AlphaExtract
2
Breakeven LECAPs BONCAPsEN Breakeven LECAPs & BONCAPs (ARS → USD) + Futures Curve This indicator plots the breakeven USD/ARS exchange rate for Argentine fixed-rate Treasury instruments LECAPs (S tickers) and BONCAPs (T tickers), showing the USD/ARS level at each maturity where holding the peso instrument would match the performance of holding dollars. What you get • Breakeven labels at (Maturity Date, Breakeven Dollar) • Automatic FX benchmarks: • Dólar MEP: BCBA:AL30 / BCBA:AL30D • Dólar Cable (CCL): BCBA:AL30 / BCBA:AL30C • Optional Custom Dollar input (1000–10000 ARS) • Optional MatbaRofex USD futures labels at their expiry dates • Optional polynomial regression curves for LECAPs, BONCAPs, and Futures (degree 1–4), with independent toggles, colors, and smoothness points Core calculations • Direct Return = (Maturity Price / Last Price) - 1 • TNA (Annualized Rate) = Direct Return × 365 / Days to Maturity • Breakeven Dollar = Current Dollar × (1 + Direct Return) Tooltip (hover labels) Ticker/type, maturity date, days to maturity, current price, maturity price (px_finish), direct return, TNA, and breakeven value. ⸻ ES Breakeven LECAPs & BONCAPs (ARS → USD) + Curva de Futuros Este indicador grafica el tipo de cambio USD/ARS de equilibrio (breakeven) para instrumentos de tasa fija del Tesoro argentino LECAPs (tickers S) y BONCAPs (tickers T). Te muestra a qué nivel de dólar, en cada vencimiento, una inversión en pesos igualaría el rendimiento de quedarse en dólares. Qué muestra • Etiquetas de breakeven en (Fecha de vencimiento, Dólar breakeven) • Referencias automáticas de tipo de cambio: • Dólar MEP: BCBA:AL30 / BCBA:AL30D • Dólar Cable (CCL): BCBA:AL30 / BCBA:AL30C • Opción de Dólar Custom (1000–10000 ARS) • Opción de mostrar futuros de USD MatbaRofex en sus vencimientos • Curvas de regresión polinómica opcionales para LECAPs, BONCAPs y Futuros (grado 1–4), con toggle, color y suavizado configurables por separado Cálculos principales • Retorno Directo = (Precio de vencimiento / Último precio) - 1 • TNA = Retorno Directo × 365 / Días al vencimiento • Dólar Breakeven = Dólar actual × (1 + Retorno Directo) Tooltip (pasar el mouse por las etiquetas) Ticker/tipo, fecha de vencimiento, días restantes, precio actual, precio de vencimiento (px_finish), retorno directo, TNA y valor de breakeven. ==================== DISCLAIMER / AVISO LEGAL ==================== This indicator is for informational and educational purposes only. Eco Valores S.A. does NOT provide investment advice or recommendations. Consult a qualified financial advisor before making investment decisions. Este indicador es solo para fines informativos y educativos. Eco Valores S.A. NO brinda asesoramiento ni recomendaciones de inversion. Consulte con un asesor financiero calificado antes de invertir. ===================================================================
Chỉ báo Pine Script®
của Eco Valores
2
LECAPS_BONCAP_LibraryLibrary "LECAPS_BONCAP_Library" getInstrumentCount() getTicker(index)   Parameters:      index (int) getTickerShort(index)   Parameters:      index (int) getMaturityPrice(index)   Parameters:      index (int) getMaturityTimestamp(index)   Parameters:      index (int) getMaturityYear(index)   Parameters:      index (int) getMaturityMonth(index)   Parameters:      index (int) getMaturityDay(index)   Parameters:      index (int) isBoncap(index)   Parameters:      index (int) isLecap(index)   Parameters:      index (int) getInstrumentType(index)   Parameters:      index (int) getDolarFuturesCount() getDolarFuturesTicker(index)   Parameters:      index (int) getDolarFuturesShort(index)   Parameters:      index (int) getDolarFuturesExpiry(index)   Parameters:      index (int) getDaysToMaturity(index)   Parameters:      index (int) getDataSummary(index)   Parameters:      index (int)
Thư viện Pine Script®
của Eco Valores
YTD % / Visible Range % TableAUTHOR: Brandon Gum DATE: 2026-01-03 // PURPOSE: // Calculates price-range metrics based on the *currently visible* // portion of the chart. Intended for table-based UI display where // values must be stable and evaluated only on the last bar. // // Originally based on Jeff Sun's ADR price data table. // // METRICS RETURNED: // - Visible High // - Visible Low // - Visible % Range = (Visible High - Visible Low) / Visible Low // - Visible ATRs = (Visible High - Visible Low) / ATR // // IMPLEMENTATION NOTES: // - Logic executes ONLY on barstate.islast to avoid state corruption. // - Visible range is recomputed atomically using a backward loop // bounded by chart.left_visible_bar_time. // - Avoids var-based accumulation and bar-by-bar resets, which are // unreliable when visible window changes. // - ATR is evaluated at the current bar (not averaged over range). // // ASSUMPTIONS / LIMITATIONS: // - Uses chart-visible time boundaries supplied by TradingView. // - Loop upper bound must be sufficiently large to cover max // expected visible bars. // - Intended for display purposes, not signal generation. // // SIDE EFFECTS: // - None. No plots, no drawings, no state persistence.
Chỉ báo Pine Script®
của gumbtg
FX Rate Bias US vs EU 2YFX Rate Bias – US vs EU (2Y) This indicator implements a rate-differential based macro bias model using the 2-year government bond yield spread between the United States and Germany. The methodology focuses on the short end of the yield curve, which primarily reflects central bank expectations rather than long-term inflation or risk premiums. By applying light smoothing and a zero-line regime framework, the script classifies market conditions into USD rate advantage or EUR rate advantage states. Calculation logic: Retrieves daily 2Y sovereign yields for the US and Germany Computes the yield differential (US − DE) Applies optional smoothing to reduce noise Uses the zero line as a regime boundary to define relative monetary bias Practical use: This tool is designed to provide directional macro context for FX analysis, particularly for EURUSD. It helps traders align technical setups with prevailing interest rate expectations, and is not intended as a standalone signal or timing indicator.
Chỉ báo Pine Script®
của emiurz
Apex ICT: Proximity & Delivery FlowThis indicator is a specialized ICT execution tool that automates the identification of Order Blocks, Fair Value Gaps, and Changes in State of Delivery (CISD). Unlike standard indicators that clutter the screen, this script uses a Proximity Logic Engine to ensure you only see tradeable levels. It automatically purges old data (50-candle CISD limit) and deletes mitigated zones the moment they are breached, leaving you with a clean, institutional-grade chart.
Chỉ báo Pine Script®
của garethsteadman47
ICT CISD+FVG+OBThis script is a high-performance ICT suite designed for traders who want a professional, "noise-free" chart. It identifies core institutional patterns—Order Blocks, Fair Value Gaps, and Changes in State of Delivery (CISD)—across multiple timeframes. The script features a proprietary Proximity Cleanup Engine that automatically deletes old or broken levels, keeping your workspace focused only on price action that is currently tradeable. It strictly follows directional delivery rules for CISD and includes a 50-candle "freshness" limit to ensure you never have to manually clear old data from your past bars. Core Features Intelligent CISD: Only triggers Bullish CISD on green candles and Bearish CISD on red candles. Proximity Filter: Automatically wipes away any levels that are "miles away" from the current price. Clean Workspace: Removes broken session highs/lows and mitigated zones instantly. Full Customization: Toggle visibility and colors for every component via the settings menu.
Chỉ báo Pine Script®
của garethsteadman47
[ElThibZ] - Futures Lot Size CalculatorI’m sharing a simple script to calculate position size for futures. You only need to enter: the risk in USD you’re willing to take the stop-loss distance in ticks The script will automatically calculate the correct position size (number of contracts) and display it in the table. This tool is designed to avoid sizing mistakes, especially on futures where contract multipliers and tick values can easily lead to incorrect risk calculations. I hope it will be as useful to you as it has been for me.
Chỉ báo Pine Script®
của ElThibZ
Adaptive Trend Envelope [BackQuant]Adaptive Trend Envelope Overview Adaptive Trend Envelope is a volatility-aware trend-following overlay designed to stay responsive in fast markets while remaining stable during slower conditions. It builds a dynamic trend spine from two exponential moving averages and surrounds it with an adaptive envelope whose width expands and contracts based on realized return volatility. The result is a clean, self-adjusting trend structure that reacts to market conditions instead of relying on fixed parameters. This indicator is built to answer three core questions directly on the chart: Is the market trending or neutral? If trending, in which direction is the dominant pressure? Where is the dynamic trend boundary that price should respect? Core trend spine At the heart of the indicator is a blended trend spine: A fast EMA captures short-term responsiveness. A slow EMA captures structural direction. A volatility-based blend weight dynamically shifts influence between the two. When short-term volatility is low relative to long-term volatility, the fast EMA has more influence, keeping the trend responsive. When volatility rises, the blend shifts toward the slow EMA, reducing noise and preventing overreaction. This blended output is then smoothed again to form the final trend spine, which acts as the structural backbone of the system. Volatility-adaptive envelope The envelope surrounding the trend spine is not based on ATR or fixed percentages. Instead, it is derived from: Log returns of price. An exponentially weighted variance estimate. A configurable multiplier that scales envelope width. This creates bands that automatically widen during volatile expansions and tighten during compression. The envelope therefore reflects the true statistical behavior of price rather than an arbitrary distance. Inner hysteresis band Inside the main envelope, an inner band is constructed using a hysteresis fraction. This inner zone is used to stabilize regime transitions: It prevents rapid flipping between bullish and bearish states. It allows trends to persist unless price meaningfully invalidates them. It reduces whipsaws in sideways conditions. Trend regime logic The indicator operates with three regime states: Bullish Bearish Neutral Regime changes are confirmed using a configurable number of bars outside the adaptive envelope: A bullish regime is confirmed when price closes above the upper envelope for the required number of bars. A bearish regime is confirmed when price closes below the lower envelope for the required number of bars. A trend exits back to neutral when price reverts through the trend spine. This structure ensures that trends are confirmed by sustained pressure rather than single-bar spikes. Active trend line Once a regime is active, the indicator plots a single dominant trend line: In a bullish regime, the lower envelope becomes the active trend support. In a bearish regime, the upper envelope becomes the active trend resistance. In neutral conditions, price itself is used as a placeholder. This creates a simple, actionable visual reference for trend-following decisions. Directional energy visualization The indicator uses layered fills to visualize directional pressure: Bullish energy fills appear when price holds above the active trend line. Bearish energy fills appear when price holds below the active trend line. Opacity gradients communicate strength and persistence rather than binary states. A subtle “rim” effect is added using ATR-based offsets to give depth and reinforce the active side of the trend without cluttering the chart. Signals and trend starts Discrete signals are generated only when a new trend regime begins: Buy signals appear at the first confirmed transition into a bullish regime. Sell signals appear at the first confirmed transition into a bearish regime. Signals are intentionally sparse. They are designed to mark regime shifts, not every pullback or continuation, making them suitable for higher-quality trend entries rather than frequent trading. Candle coloring Optional candle coloring reinforces regime context: Bullish regimes tint candles toward the bullish color. Bearish regimes tint candles toward the bearish color. Neutral states remain visually muted. This allows the chart to communicate trend state even when the envelope itself is partially hidden or de-emphasized. Alerts Built-in alerts are provided for key trend events: Bull trend start. Bear trend start. Transition from trend to neutral. Price crossing the trend spine. These alerts support hands-off trend monitoring across multiple instruments and timeframes. How to use it for trend following Trend identification Only trade in the direction of the active regime. Ignore counter-trend signals during confirmed trends. Entry alignment Use the first regime signal as a structural entry. Use pullbacks toward the active trend line as continuation opportunities. Trend management As long as price respects the active envelope boundary, the trend remains valid. A move back through the spine signals loss of trend structure. Market filtering Periods where the indicator remains neutral highlight non-trending environments. This helps avoid forcing trades during chop or compression. Adaptive Trend Envelope is designed to behave like a living trend structure. Instead of forcing price into static rules, it adapts to volatility, confirms direction through sustained pressure, and presents trend information in a clean, readable form that supports disciplined trend-following workflows.
Chỉ báo Pine Script®
của BackQuant
14
Q# ML Logistic Regression Indicator [Lite] Q TechLabs MLLR Lite — Machine Learning Logistic Regression Trading Indicator © Q# Tech Labs 2025 Developed by Team Q TechLabs Overview Q# MLLR Lite is an open-source, lightweight TradingView indicator implementing a logistic regression model to generate buy/sell signals based on engineered price features. This “lite” version is designed for broad community access and serves as a foundation for the upcoming Pro version with advanced features and integration. Features Logistic Regression-based buy/sell signal generation Customizable price source input (Open, High, Low, Close, HL2, HLC3, OHLC4) Adjustable signal threshold and smoothing parameters Signal confidence plotted in a separate pane Alert conditions for buy and sell signals Fully documented, clean Pine Script (v6) code for easy customization Installation Open TradingView and navigate to the Pine Script editor Create a new script and paste the full content of the Q# MLLR Lite Pine Script Save and add to chart Configure inputs as needed for your trading style Licensing Q# MLLR Lite is provided under the MIT License, promoting open use, modification, and community collaboration with attributi Q# MLLR Lite — Machine Learning Logistic Regression Trading Indicator © Q# Tech Labs 2025 — Developed by Team Q# Overview Q# MLLR Lite is an open-source, lightweight TradingView indicator implementing a logistic regression model to generate buy/sell signals based on engineered price features. This “lite” version is designed for broad community access and serves as a foundation for the upcoming Pro version with advanced features and integration. Features Logistic Regression-based buy/sell signal generation Customizable price source input (Open, High, Low, Close, HL2, HLC3, OHLC4) Adjustable signal threshold and smoothing parameters Signal confidence plotted in a separate pane Alert conditions for buy and sell signals Fully documented, clean Pine Script (v6) code for easy customization Installation Open TradingView and navigate to the Pine Script editor Create a new script and paste the full content of the Q# MLLR Lite Pine Script Save and add to chart Configure inputs as needed for your trading style Licensing Q# MLLR Lite is provided under the MIT License, promoting open use, modification, and community collaboration with attribution. Copyright (c) 2025 Q# Tech Labs Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
Chỉ báo Pine Script®
của QTechLabsInfo