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All-in-one trend clarityTrendLens is a multi-layer, all-in-one overlay indicator designed to visually detect and filter market direction — not a buy/sell strategy. It highlights early trend shifts based on candle behavior, then supports that view using Pivot High/Low structure, three customizable EMAs, and a visible daily session window to focus on active market hours. What’s included (All inside one indicator) Structural Trend Candles If price closes above the highest high of the previous N bars → candle turns white (bullish structural breakout). If price closes below the lowest low of the previous N bars → candle turns black (bearish structural breakdown). Pivot High / Pivot Low Markers Detects swing highs/lows using adjustable left/right bars (default 7) and plots small gray triangle markers on the chart. Active Session Window Highlights a fixed daily time window (default 06:00–18:00 UTC) with a transparent green background to visually mark the active trading session. 3 Customizable EMAs EMA Fast (default 10) EMA Mid (default 20) EMA Long (default 100) Each EMA supports custom length, source, color, and thickness. How to use it Use white/black candles as a quick trend filter and early structure shift cue. Use EMA100 as the main trend bias reference; use EMA10/EMA20 positioning to gauge momentum. Use Pivot High/Low to spot structure levels for potential support/resistance and risk management. Enable the session highlight to focus analysis on high-activity hours. Disclaimer This indicator is a technical analysis helper, not a trading strategy. It does not provide buy/sell recommendations. You are responsible for your own trade decisions and risk management.
Pine Script® インジケーター
SmartChartXの投稿
XAU PDH-PDL REV (Buy the Dip)Indicator Description – Buy the Dip first, then Continuation This indicator is designed for trading Gold (XAUUSD) with an institutional, pullback-focused mindset. It prioritises **REV (Reversal) signals** to *buy the dip* or *sell the rip* after a **significant ATR-based pullback**, without relying on EMA reclaim (so strong trends aren’t missed). Only when no valid reversal is present will it allow **CONT (Continuation) signals**, aligned with trend and EMA pullbacks. Key targets are based on **Daily, Weekly, or Rolling liquidity levels**, and all prices are shown as **whole numbers** for clarity. Session awareness (NZ time) helps contextualise signals, while cooldown logic reduces noise and over-trading.
Pine Script® インジケーター
abesamisjの投稿
Strength Relative to XXX [Hysteresis Smoothed]Strength Relative to XXX █ OVERVIEW This versatile indicator measures the relative strength of the current charted asset against any user-selected benchmark symbol (e.g., BTC, ETH, SP:SPX, TVC:GOLD, or any other asset). Green fill = Current asset outperforming the benchmark (bullish relative strength). Red fill = Current asset underperforming the benchmark (bearish relative weakness). Perfect for rotation strategies across crypto, stocks, forex, and commodities — quickly identify assets gaining momentum edge over a chosen benchmark. █ HOW IT WORKS • Relative Ratio : Calculates current close / benchmark close for normalized comparison. • Smoothing : Applies a Simple Moving Average (SMA) to the ratio (adjustable length). • Oscillator : Plots deviation from the SMA, centered around zero. • Hysteresis Enhancement : Adds a small relative threshold (~0.03% default) to prevent rapid color flips from minor noise. Color persists until a convincing cross — stable blocks without lag. █ FEATURES & INPUTS • Compare to : Symbol input for any benchmark (match exchange for accuracy). • MA Length : Smoothing period (default 10). • Relative Hysteresis Threshold : Noise filter strength (default 0.0003; tweak for responsiveness vs. stability). █ USAGE TIPS • Apply to ALT/BTC pairs for crypto rotations, stocks vs. SP:SPX for sector strength, or any custom comparison. • Works on all timeframes — ideal for short-term scans on 4H/daily. • Green zones = potential outperformance; red = caution. • Combine with volume or momentum for confluence. This refined relative strength oscillator delivers clean, reliable visuals in volatile markets.
Pine Script® インジケーター
ThroatCloggerの投稿
MA RespectRatio RespectRatio A Structural Moving Average Quality Indicator What is RespectRatio RespectRatio is a statistical indicator designed to evaluate *how reliably a stock respects a specific moving average over time. Instead of asking “Did price touch the MA?”, it answers a more meaningful question: ⁠Does this moving average actually function as support for this stock consistently and structurally? The indicator focuses on *historical behavior, not short-term signals, and is intended to support buy / hold / reduce decisions rather than precise trade timing. Why RespectRatio Exists Many stocks frequently touch moving averages, but only some of them: ⁠Rebound cleanly ⁠ ⁠Hold above the average ⁠Do so repeatedly over long periods RespectRatio was built to separate real support from visual noise. Core Concept RespectRatio treats every interaction between price and the moving average as a measurable event. Each event ends in one of two outcomes: •⁠ ⁠Bounce — price respects the moving average •⁠ Break — price fails and breaks below it Over time, these outcomes form a probability profile of how the stock behaves around that average. How an Event Starts An event begins when price meaningfully interacts with the moving average, either by: •⁠ ⁠Entering a volatility-adjusted proximity zone around the MA, or •⁠ ⁠Crossing below the MA (including gap-downs) The proximity zone is adaptive and defined as: k = ATR% × kMultiplier This keeps the definition of “close enough” consistent across assets and volatility regimes. Event Outcomes Bounce (Respect) An event is classified as a Bounce when price: •⁠ ⁠Moves back above the moving average •⁠ ⁠Clears a minimal buffer above it •⁠ ⁠*Maintains that position for a defined number of sessions. This confirms that the moving average acted as real support not a temporary pause. Break (Failure) An event becomes a Break when price: •⁠ ⁠Remains below the moving average for too long, or •⁠ ⁠Falls significantly below it and fails to reclaim it within a short window A Break signals structural weakness at that average. Noise Control To avoid statistical distortion: •⁠ ⁠Only one outcome per event is recorded •⁠ ⁠A cooldown period prevents immediate re-counting of the same struggle •⁠ ⁠Each event is counted once, regardless of intraday noise This ensures clean, independent data points. The Final Metric The indicator produces a single core metric: RespectRatio = Bounces / (Bounces + Breaks) Calculated over a rolling historical window. How to Interpret RespectRatio •⁠ ⁠High RespectRatio The moving average has historically acted as reliable support → suitable for accumulation or holding strategies •⁠ Low RespectRatio The moving average is frequently violated → caution when relying on it as support RespectRatio does not predict future price, but measures structural trustworthiness. What RespectRatio Is Not •⁠ ⁠Not a buy/sell signal generator •⁠ ⁠Not a trend-following indicator •⁠ ⁠Not a momentum oscillator It is a contextual filter* that improves decision quality. Typical Use Cases •⁠ ⁠Evaluating whether a stock deserves to be bought near a long-term MA •⁠ ⁠Comparing multiple stocks using the same moving average •⁠ ⁠Filtering candidates before applying other strategies •⁠ ⁠Long-term portfolio decision support One-Sentence Summary RespectRatio quantifies how often the market actually respects a moving average — turning a visual assumption into measurable probability.*
Pine Script® インジケーター
noamz9400の投稿
Volume-Weighted RSI [VWRSI 2D Pro]A modular, volume-weighted RSI indicator built for clarity and control. ✅ Profile-based auto modes (Scalping → Macro) ✅ Toggleable Buy/Sell signals with strict mode ✅ RSI MA overlays for smoother entries Buy Signal RSI crosses above RSI MA RSI > 50 (or > 55 in strict mode) Sell Signal RSI crosses below RSI MA RSI < 50 (or < 45 in strict mode) Strict mode filters out weak signals for higher conviction entries. Volatility-Adaptive RSI Thresholds: Traditional RSI uses static levels (70/30). VWRSI Pro replaces these with dynamic bands: 🔹dynHigh = mean + mult × deviation 🔹 dynLow = mean − mult × deviation Technical write-up can be found here: github.com
Pine Script® インジケーター
followmytradingjournalの投稿
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.
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DskyzInvestmentsの投稿
2
Trend Harmony🚀 Trend Harmony: Multi-Timeframe Momentum & Trend Dashboard Trend Harmony is a sophisticated multi-timeframe (MTF) analysis tool designed to help traders identify high-probability setups by spotting "Market Harmony." Instead of flipping through charts, this indicator synthesizes RSI momentum and EMA trend structures from four different time horizons into a single, intuitive dashboard. 🔍 How It Works The core philosophy of this indicator is that the most powerful moves happen when short-term momentum aligns with long-term trend structure. The script tracks four user-defined timeframes simultaneously. 1. The Trend Scoring Engine The indicator evaluates the relationship between a Fast EMA (default 20) and a Slow EMA (default 50) across all active timeframes. Bullish Alignment: Fast EMA > Slow EMA. Bearish Alignment: Fast EMA < Slow EMA. 2. The Harmony Summary At the bottom of the dashboard, the "Summary" status calculates the total "Harmony" of the market: 🚀 FULL BULL HARMONY: All selected timeframes are in a bullish trend. 📉 FULL BEAR HARMONY: All selected timeframes are in a bearish trend. ⚠️ CAUTION (Overbought/Oversold): Triggered when the market is in "Full Harmony" but RSI levels suggest the price is overextended (>70 or <30). This warns you not to "chase" the trade. Neutral/Mixed: Timeframes are in conflict (e.g., 15m is bullish but Daily is bearish). 🛠 Key Features Unified RSI Pane: View four RSI lines on one chart to spot divergences or "clusters" where all timeframes bottom out at once. Dynamic Table: Real-time tracking of: Price vs EMA: Instant visual (▲/▼) showing if price is above/below your key averages. Smart RSI Coloring: RSI values turn Green during "Power Zones" (0–30 or 50–70) and Red otherwise. Full Customization: Change timeframes (1m, 5m, 1H, D, etc.), EMA lengths, and RSI parameters to fit your strategy. 📈 Trading Strategy Tips Wait for the Sync: The "Full Harmony" status is your signal that the "tide" is moving in one direction. Look for long entries when the status is Green and short entries when it is Red. The Pullback Entry: When the summary says "Caution (Overbought)," wait for the RSI lines to cool down toward the 50 level before entering the trend again. RSI Clustering: When all four RSI lines converge at extreme levels (30 or 70), a massive volatility expansion is usually imminent.
Pine Script® インジケーター
ebecihalilの投稿
TrintityTrendIntroducing TrinityTrend A multi-signal indicator combining: Candle TrendStrength SuperTrend logic TTM Squeeze detection Built for clarity, momentum, and volatility awareness—across any timeframe. TrendStrength Mode Candle coloring reflects directional conviction. Strong uptrend Strong downtrend Neutral or indecisive Helps traders stay with momentum and avoid chop. SuperTrend Overlay SuperTrend Logic Dynamic trailing stop based on volatility. 🟩 Price above = bullish bias 🟥 Price below = bearish bias Great for swing entries and exits. TTM Squeeze Detection TTM Squeeze Mode Detects compression zones before breakout. Squeeze on = buildup (You can change the color of this) Pairs well with TrendStrength for timing entries. Multi-Timeframe Versatility Multi-Timeframe Ready: Intraday scalping Daily swing setups Weekly macro bias Toggle modes to match your strategy
Pine Script® インジケーター
followmytradingjournalの投稿
ATR Price ZoneThe ATR Price Zone is an indicator which takes the Daily Average True Range of a stock and shows how high and low the price of the stock could possibly go from the opening price. Key features: The ATR Price Zone is an indicator which takes the Daily Average True Range of a stock and shows how high and low the priced the stock could possibly go from the opening price. Key features: ATR Price Zone uses zones looking forward to help strategize possible movements in price. This indicator is customizable with zones, horizontal lines, a quick reference chart and colors. The indicator continues to move forward with the chart. It references the Daily True Average Range regardless of which Time Frame you are using. It also references the opening candle with a blue arrow when using less than daily time frames. Create by BothwellTrader
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bothwellpdxの投稿
Vortex Trend Matrix [JOAT]Vortex Trend Matrix - Multi-Factor Trend Confluence System Introduction and Purpose Vortex Trend Matrix is an open-source overlay indicator that combines Ichimoku-style equilibrium analysis with the Vortex Indicator to create a comprehensive trend confluence system. The core problem this indicator solves is that single trend indicators often give conflicting signals. Price might be above a moving average but momentum might be weakening. This indicator addresses that by combining five different trend factors into a single composite score, making it easy to identify when multiple factors align for high-probability trend trades. Why These Components Work Together Each component measures trend from a different perspective: 1. Cloud Position - Price above/below the equilibrium cloud indicates overall trend bias. The cloud acts as dynamic support/resistance. 2. TK Relationship - Conversion line vs Base line (like Tenkan/Kijun in Ichimoku). Conversion above Base = bullish momentum. 3. Lagging Span - Current price compared to price N bars ago. Confirms whether current move has follow-through. 4. Vortex Indicator - VI+ vs VI- measures directional movement strength. Provides momentum confirmation. 5. Base Direction - Whether the base line is rising or falling. Indicates medium-term trend direction. How the Trend Score Works float trendScore = 0.0 // Cloud position (+2/-2) trendScore += aboveCloud ? 2.0 : belowCloud ? -2.0 : 0.0 // TK relationship (+1/-1) trendScore += conversionLine > baseLine ? 1.0 : conversionLine < baseLine ? -1.0 : 0.0 // Lagging span (+1/-1) trendScore += laggingBull ? 1.0 : laggingBear ? -1.0 : 0.0 // Vortex (+1.5/-1.5) trendScore += vortexBull ? 1.5 : vortexBear ? -1.5 : 0.0 // Base direction (+0.5/-0.5) trendScore += baseDirection * 0.5 Score ranges from approximately -6 to +6: - +4 or higher = STRONG BULL - +2 to +4 = BULL - -2 to +2 = NEUTRAL - -4 to -2 = BEAR - -4 or lower = STRONG BEAR Signal Types TK Cross Up/Down - Conversion line crosses Base line (momentum shift) Base Direction Change - Base line changes direction (medium-term shift) Strong Bull/Bear Trend - Score reaches +4/-4 (high confluence) Dashboard Information Trend - Overall status with composite score Cloud - Price position (ABOVE/BELOW/INSIDE) TK Cross - Conversion vs Base relationship Lagging - Lagging span bias Vortex - VI+/VI- relationship VI+/VI- - Individual vortex values How to Use This Indicator For Trend Following: 1. Enter long when trend score reaches +4 or higher (STRONG BULL) 2. Enter short when trend score reaches -4 or lower (STRONG BEAR) 3. Use cloud as dynamic support/resistance for entries For Momentum Timing: 1. Watch for TK Cross signals for entry timing 2. Base direction changes indicate medium-term shifts 3. Vortex confirmation adds conviction For Risk Management: 1. Exit when trend score drops to neutral 2. Use cloud edges as stop-loss references 3. Reduce position when score weakens Input Parameters Conversion Period (9) - Fast equilibrium line Base Period (26) - Slow equilibrium line Lead Span Period (52) - Cloud projection period Displacement (26) - Cloud and lagging span offset Vortex Period (14) - Period for vortex calculation VI+ Strength (1.10) - Threshold for strong bullish vortex VI- Strength (0.90) - Threshold for strong bearish vortex Timeframe Recommendations 4H-Daily: Best for equilibrium-based analysis 1H: Good for intraday trend following Lower timeframes may require adjusted periods Limitations Equilibrium calculations have inherent lag Cloud displacement means signals are delayed Works best in trending markets May whipsaw in ranging conditions Open-Source and Disclaimer This script is published as open-source under the Mozilla Public License 2.0 for educational purposes. This indicator does not constitute financial advice. Trend analysis does not guarantee profitable trades. Always use proper risk management. - Made with passion by officialjackofalltrades
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officialjackofalltradesの投稿
MA 50/200This MA 50/200 indicator is a classic TradingView overlay that plots the 50-period Simple Moving Average (SMA) in solid green and the 200-period SMA in solid red directly on the price chart. It highlights major trend-reversal signals: A Golden Cross occurs when the faster 50-period SMA crosses above the slower 200-period SMA, often interpreted as a bullish signal suggesting potential upward momentum. This is marked by a prominent green cross symbol (linewidth 5) plotted at the level of the 200 SMA on the bar where the crossover happens. A Death Cross occurs when the 50-period SMA crosses below the 200-period SMA, often seen as a bearish signal indicating potential downward momentum. This is marked by a red cross symbol (slightly translucent for subtlety) at the 200 SMA level on the crossover bar. The layout keeps the chart clean and focused: continuous thick lines for the moving averages with clear, oversized cross markers only at crossover points to make Golden and Death Cross events instantly visible without clutter.
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CryptoHitchhikerの投稿
Adjusted RSI - [JTCAPITAL]Adjusted RSI – is a modified and enhanced way to use the Relative Strength Index (RSI) combined with double normalization, adaptive exponential smoothing, and range compression to create a smoother, more readable, and more structurally consistent momentum oscillator for Trend-Following and momentum analysis. This indicator is designed to solve several common RSI issues at once: Excessive noise in raw RSI values Inconsistent scaling across different market conditions Difficulty identifying true momentum shifts versus random fluctuations By re-centering, compressing, normalizing, and smoothing RSI data twice , this script produces a highly refined momentum curve that reacts smoothly while still respecting directional changes. The indicator works by calculating in the following steps: Raw RSI Calculation The script begins by calculating a standard RSI using the selected RSI Length . This RSI is based on the closing price and measures relative strength by comparing average gains and losses over the defined period. RSI Re-Centering After the RSI is calculated, the script subtracts 50 from the RSI value. This converts the RSI from its native scale into a centered oscillator ranging around 0 , making positive values bullish momentum and negative values bearish momentum. Initial RSI Smoothing The re-centered RSI is then smoothed using a Simple Moving Average (SMA) over the defined RSI Smoothing Length . This step removes high-frequency noise and stabilizes short-term RSI fluctuations before further processing. Range Compression (Clipping) To prevent extreme outliers from dominating future calculations, the RSI values are clipped: Values below -10 are forced to -10 Values above +10 are forced to +10 This creates a controlled and consistent RSI range, ensuring later normalization behaves reliably. First Normalization (Min-Max Scaling) The clipped RSI values are normalized over the selected Smoothing Length : The lowest RSI value in the window is detected The highest RSI value in the window is detected Current RSI is scaled to a 0–100 range based on this dynamic range This allows the indicator to adapt automatically to changing volatility and momentum environments. First Adaptive Smoothing The normalized RSI is then smoothed using a custom exponential smoothing formula controlled by the Smoothing Factor . This smoothing behaves similarly to an EMA but allows explicit control over responsiveness. Second Normalization The smoothed values undergo a second min-max normalization over the same length. This further stabilizes the oscillator and ensures consistent amplitude and structure, regardless of market regime. Second Adaptive Smoothing A second exponential smoothing pass is applied to the normalized data, further refining the curve and reducing residual noise. Final Re-Centering Finally, the indicator subtracts 50 from the smoothed normalized values, re-centering the oscillator around zero . This produces the final Adjusted RSI line used for visualization and analysis. Common interpretations for use include: Bullish Momentum : When the Adjusted RSI is above zero and rising, indicating strengthening bullish pressure. Bearish Momentum : When the Adjusted RSI is below zero and falling, indicating strengthening bearish pressure. Momentum Shifts : A change in slope (from falling to rising or vice versa) often signals an early momentum transition. Divergences : Differences between price direction and Adjusted RSI direction can highlight potential reversals. Because the indicator is normalized and smoothed, it pairs exceptionally well with: Trend filters (moving averages, trend lines) Volatility filters Higher-timeframe confirmation Features and Parameters: RSI Length Defines the lookback period for the initial RSI calculation. RSI Smoothing Length Controls the SMA smoothing applied directly to the re-centered RSI. Smoothing Length Determines the lookback window used for both normalization passes. Smoothing Factor Controls the responsiveness of the adaptive exponential smoothing. Lower values = smoother, slower reaction Higher values = faster, more responsive reaction Specifications: Relative Strength Index (RSI) RSI is a momentum oscillator that measures the speed and magnitude of recent price changes. By re-centering RSI around zero, the script converts it into a directional momentum oscillator that is easier to interpret for trend-following. Simple Moving Average (SMA) The SMA reduces short-term fluctuations in RSI, ensuring that only meaningful momentum changes proceed to later calculations. Range Clipping By limiting RSI values to a defined range, extreme spikes are prevented from skewing normalization. This keeps the indicator stable across different assets and timeframes. Min-Max Normalization Normalization rescales values into a fixed range (0–100), allowing momentum behavior to remain consistent regardless of volatility conditions. Adaptive Exponential Smoothing This smoothing technique gradually adjusts values toward new data based on the smoothing factor. It allows the indicator to remain smooth while still reacting to genuine momentum shifts. Double Normalization and Double Smoothing Applying normalization and smoothing twice significantly improves structural stability. The result is a refined oscillator that filters noise without sacrificing trend awareness. Why This Combination Works By combining RSI with controlled compression, adaptive smoothing, and dynamic normalization, this indicator transforms raw momentum data into a highly structured and trend-aligned oscillator. The result is an RSI-based tool that: Reduces noise Adapts to volatility Maintains consistent scaling Highlights true momentum direction This makes the Adjusted RSI particularly effective for swing trading, trend confirmation, and momentum-based strategies across all markets and timeframes. Enjoy!
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JTCapitalNLの投稿