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TrendSurfer Lite TrendSurfer Lite ⚡ Advanced Multi-Signal Trading Indicator for Precision Market Analysis TrendSurfer Pro LITE is a comprehensive trading system combining multiple technical analysis tools into one powerful indicator. Designed for traders seeking high-probability setups with customizable filters. Key Features: 📊 Core Signals Triangle Signals (▲▼): Volume-weighted momentum entries with 10-level volume scoring Master Trend System (△▽): Multi-EMA confirmation with RSI validation Order Blocks (🟩🟥): Smart money institutional zones with rejection detection Take Profit System (🎯): 8-indicator confluence system (RSI, Stochastic, Supertrend, CCI, MACD, BB, EMA Cross, Price Action) 🎯 Rejection Signals Master Trend Rejections: Dynamic support/resistance from trend lines EMA750 Rejections (White "R"): Major trend filter bounces VWAP Rejections (Pink "W"): Institutional level reactions Butterworth Filter Rejections (🟡): Advanced smoothing algorithm reversals Session Rejections (⚡): Tokyo/London/NY session high/low bounces Session Midline Rejections (Orange "M"): Half-range mean reversion 🌍 Session Analysis Tokyo Session (💴): Asian market range with automatic extensions London Session (💶): European volatility zones New York Session (💵): US market key levels Auto-adjusting timezone with UTC offset support 🔧 Advanced Filters EMA750 Master Filter: Global trend alignment for all signals VWAP Filter: Institutional bias confirmation Yellow Box Filter (🟨): Consolidation zone proximity detection 3 Time Filters: Customizable trading windows with visual backgrounds Volume Filter: Signal strength validation (6-10 scale) 📈 Visual Tools VWAP Purple Candles: Special candle coloring for VWAP crosses above EMA750 Stochastic-based Candle Colors: Overbought/oversold visual cues Previous Day Close Line: Key reference level Master Trend Table: Real-time multi-indicator dashboard ⚙️ Customization Full color customization for all elements Adjustable line thickness and transparency Configurable alert system for every signal type 19 independent alert conditions Best For: Intraday scalping and swing trading Multi-timeframe analysis Confluence-based trading strategies Institutional level detection Version 1.0 | Clean interface | Maximum flexibility | Professional-grade signals
Индикатор Pine Script®
от jake_theboss
Sameer Bandhara AlertsThis Sameer Bandhara (SB Trader) indicator is a dynamic trailing stop-loss system based on the Average True Range (ATR). Here's a comprehensive breakdown: It uses ATR to create an adaptive trailing stop that adjusts to market volatility, generating buy/sell signals when price breaks through this dynamic stop level. Forex/Stocks: Key Value 1.5-2.5, ATR Period 14-20 Crypto: Key Value 2.0-3.0 (higher volatility) Timeframes: 1H and above (reduces noise)
Индикатор Pine Script®
от sameer-1
ES Sessions - Asia / London / NY / Break This indicator draws clean, outline-only session boxes for ES futures, covering Asia, London, New York (RTH), Overnight, and the Daily Break. Most session indicators add too much visual noise. Heavy background shading, overlapping colors, excessive lines, or extra calculations clutter the chart and distract from price. This script is intentionally different. Each session is displayed as a simple price range outline showing the high and low of the session, with a single label placed at the top center. There is no background shading and no unnecessary highlights or metrics. Sessions are defined using CME Central Time so they remain accurate, while Trading View automatically converts them to your chart’s local time. The goal is clarity. See where each session starts and ends, understand its range, and keep the focus on price action without distraction. Ideal for traders who want structure without clutter. Socials - @burntledger
Индикатор Pine Script®
от burntledger
1
Trump Trade Master XAUUSD - v2Trump Trade Master XAUUSD - Geopolitical Gold Indicator (2026 Edition) Overview: This indicator is a specialized tool for XAUUSD (Gold) traders, engineered to navigate the unique market dynamics of the 2026 "Trump Trade" era. It utilizes Intermarket Analysis to correlate gold prices with U.S. economic policies, tariff news, and geopolitical events (such as energy-related operations in Venezuela and defense sector shifts). Key Features: Trump Sentiment Dashboard: A real-time monitor located at the top-right, tracking market aggression via the Energy (XLE) and Defense (ITA) sectors. Gold-DXY Correlation Monitor: Dynamically tracks the relationship between Gold and the US Dollar. During geopolitical crises, a positive correlation often signals a powerful "Safe Haven" move. Clean Signal Engine (V3): Designed for clarity. Signal labels (BUY/SELL) appear only on the first bar of a trend shift to prevent chart clutter while maintaining background color zones for trend bias. Macro Pressure Analysis: Integrates US10Y Yield data to identify high-probability sell zones when dollar strength and rising interest rates create headwinds for gold. How to Read the Signals: 🟢 BUY WAR/INF: Triggered when geopolitical tensions rise and gold shows strength, often decoupling from its usual inverse relationship with the dollar. 🔴 SELL STRONG DXY: Triggered when "America First" economic policies lead to a surging Dollar Index (DXY) and rising yields, pressuring gold prices downward. 📊 Dashboard: Use the "Trump Sentiment" (Aggressive/Stable) and "Gold-DXY Corr" values to confirm the macro-trend before entering a trade. Recommended Settings: Asset: XAUUSD / GOLD Timeframes: 1H (Hourly) and 4H (4-Hour) for the most reliable macro-trend signals. Usage: Best used alongside price action and global news events. 📱 Join our community for strategy updates: Add Line: @191ricya Disclaimer: Trading involves significant risk. This indicator is a tool for statistical and macro-logical analysis and does not guarantee profits. Always use proper risk management.
Индикатор Pine Script®
от AIsuperbullish
2-Year Simple Moving Average (SMA)2-Year Simple Moving Average (SMA) This indicator plots a 2-year Simple Moving Average (SMA) of price, designed to highlight long-term market trends and major support or resistance zones. The 2-year SMA automatically adapts to the chart’s timeframe: Daily charts: Uses either trading days (≈252 per year) or calendar days (365 per year) Weekly charts: Uses 52 weeks per year Monthly charts: Uses 12 months per year Intraday charts: Estimates bars per year based on the selected timeframe An optional secondary smoothing moving average can be applied to the 2-year SMA itself, with multiple smoothing types available: SMA EMA SMMA (RMA) WMA VWMA SMA with optional Bollinger Bands When “SMA + Bollinger Bands” is enabled, volatility bands are calculated using the standard deviation of the 2-year SMA, helping visualize trend stability and expansion. This indicator is best suited for: Identifying long-term trend direction Locating macro support and resistance Filtering short-term market noise Assessing price position relative to long-term fair value Ideal for investors and swing traders seeking a high-timeframe trend reference rather than short-term trade signals. By JezzaBTC
Индикатор Pine Script®
от JezzaBTC
Z Score FilterComposite Risk Filter This indicator works because it aggregates several independent but structurally important stress channels (currency strength, rates, equity volatility, bond volatility, and credit conditions) into a single normalized measure. Each input is transformed into a z-score, meaning the composite does not care about absolute levels, narratives, or regimes; it only measures whether conditions are tightening or easing relative to what has been normal recently. That makes the output robust to inflation, secular trends, and structural shifts that break simpler correlations. What the indicator captures is not direction but constraint. Markets do not move because risk is “on” or “off”; they move because certain behaviors are more or less permitted under prevailing financial conditions. By identifying when systemic pressure is elevated, relaxed, or neutral, the indicator helps align trade expectations with the environment price is operating in. When used as a filter — not a signal — it reduces false confidence, improves expectancy selection, and keeps price in the primary role where it belongs.
Индикатор Pine Script®
от BLKMonk
1m Volume Spike vs Last 50 with minimum price and candle rangeThis indicator looks for very strong 1-minute momentum bursts backed by abnormal volume, while filtering out low-priced stocks. It highlights and alerts when a stock: Is trading above $20 Prints a 1-minute volume spike (≥ 10× its recent 1-minute average) Shows strong price conviction, where the current 1-minute candle body is almost as large as the previous candle’s body When all of this happens together, the candle is painted yellow and an alert can fire.
Индикатор Pine Script®
от suparna_datta
9:45 AM Candle HighlighterThis script is a powerful visual aid for traders who use the 9:45 AM "Truth Candle" to dictate their morning bias. By automatically highlighting this specific 15-minute candle, it removes the guesswork and allows you to focus purely on execution when your confluences align.Here are a few ways to describe your script, depending on where you are sharing it (e.g., TradingView, Discord, or Social Media).Option 1: The Professional "Script Description" (Best for TradingView)Title: 9:45 AM Opening Range Anchor & Zone HighlighterOverview:This script identifies and highlights the 9:45 AM 15-minute candle (the close of the opening range) in a distinct Yellow Zone. In institutional trading, this candle often marks the end of opening manipulation and the beginning of the day's "real" trend.Key Features:Automatic Highlighting: Instantly colors the 9:45 AM candle yellow for easy identification.High/Low Plotting: Clearly marks the boundaries of the 15-minute range.Equilibrium Line: Automatically calculates and displays the 50% Level (Mean Threshold) of the candle for premium/discount entries.How to Trade This Script:The Breakout (Displacement): Watch for a strong close above or below the yellow zone, creating a Fair Value Gap (FVG).The Retest: Wait for price to return to the 50% level of the yellow candle or the newly formed FVG.The Trigger: Enter your trade once an Engulfing Candle prints in the direction of the displacement.Option 2: The "Strategy Guide" Style (Best for a Community or PDF)The "Yellow Candle" Strategy: Trading the Institutional FootprintThe 9:45 AM candle is the first candle of the day that institutions use to "set the trap" or "reveal the trend." This script paints that candle yellow to help you stay disciplined.Entry Model A: The 50% EquilibriumMany high-probability setups involve the market returning to the "midpoint" of the 9:45 candle before continuing the move.Action: If price breaks the high of the yellow candle, set a Limit Order at the 50% mark of that candle.Why: This offers a tighter stop-loss and a better Risk-to-Reward ratio.Entry Model B: The Displacement & FVG ConfluenceStep 1: Look for a "Displacement" (a large, energetic candle) breaking out of the yellow zone.Step 2: Ensure an FVG is left behind.Step 3: Enter when price taps the FVG or forms an Engulfing Candle against the yellow zone high/low.Option 3: The "Elevator Pitch" (Short & Punchy for Social Media)"I simplified my morning routine by creating a script that highlights the 9:45 AM 'Truth Candle' in yellow. No more squinting at the charts to find the opening range high/low. 🎯It plots the zone and the 50% equilibrium level automatically. I just wait for the displacement, look for the Fair Value Gap, and let the Engulfing candle be my trigger. Simple, visual, and effective. 📈✨"Technical Summary for your UsersFeaturePurposeYellow HighlightIdentifies the 9:45 AM (15-min) Institutional Anchor.High/Low LevelsDefines the "Support & Resistance" for the AM session.50% LevelProvides a "Discount" entry point for retracement traders.Confluence FilterDesigned to be used with FVG and Engulfing patterns for 90%+ clarity
Индикатор Pine Script®
от Stewnomics
Laplace Transform Oscillator Pro主要功能： 拉普拉斯變換近似：使用指數衰減權重來模擬拉普拉斯域的平滑效果 震盪器(LTO)：顯示價格與拉普拉斯平滑值的差異 信號線：提供交易信號的參考線 柱狀圖：顯示LTO與信號線的差異 參數說明： Length：拉普拉斯變換的窗口長度（預設14） Alpha：衰減係數，控制平滑程度（預設0.3，越小越平滑） Signal Line Length：信號線的EMA週期（預設9） 交易信號： 🟢 買入信號：LTO向上穿越信號線時出現綠色三角形 🔴 賣出信號：LTO向下穿越信號線時出現紅色三角形 背景顏色會根據趨勢變化（綠色=看漲，紅色=看跌） 功能： 資訊面板：顯示當前LTO值、訊號線、趨勢強度和距離上次訊號的K棒數 視覺標記：🚀(買入) 🔻(賣出)更清楚的標示 門檻線：綠色/紅色虛線顯示訊號觸發區域 ⚙️ 建議參數調整： 提高Signal Threshold(0.5→1.0)可進一步減少訊號 增加Min Bars Between Signals(5→10)延長間隔 調整Length(21)可改變靈敏度 Main functions： Laplace transform approximation: Use exponential attenuation weights to simulate the smoothing effect of the Laplace domain Oscillator (LTO): Shows the difference between the price and the Laplace smoothing value Signal line: A reference line that provides trading signals Histogram: Shows the difference between LTO and signal line Parameter description： Length: The window length of the Laplace transform (preset 14) Alpha: Attenuation coefficient, control the degree of smoothing (preset 0.3, the smaller the smoother) Signal Line Length: The EMA cycle of the signal line (default 9) Trading signals： 🟢Buy signal: A green triangle appears when LTO crosses the signal line upward 🔴Sell signal: A red triangle appears when LTO crosses the signal line downwards The background color will change according to the trend (green = bullish, red = bearish) function： Information panel: displays the current LTO value, signal line, trend strength, and the number of K bars from the last signal Visual marking:清楚(buy) 🔻 (sell) Clearer marking Threshold line: Green/red dotted line shows the signal trigger area ️️ Recommended parameter adjustment： Increasing the Signal Threshold (0.5→1.0) can further reduce the signal Increase Min Bars between Signals (5→10) to extend the interval Adjust the length (21) to change the sensitivity
Индикатор Pine Script®
от YOWYI
Advanced Volume & Liquidity SuiteThe Institutional Code is an advanced trading system designed to reveal the footprint of "Smart Money" in the Futures and Indices markets. Unlike traditional indicators that track price, this algorithm tracks Real Volume and Liquidity, comparing retail data with institutional (CME) data to identify zones of manipulation and absorption. This script transforms your chart into an institutional command board, ideal for trading NQ (Nasdaq), ES (S&P 500), and YM (Dow Jones) with surgical precision.
Индикатор Pine Script®
от sebaztiansaenzX
BBQ Levels - Options Spread Diversification GridOverview BBQ Levels (also known as "The Grill") is a price-level tracking indicator designed for options traders who use iron condors, put credit spreads, or other spread strategies. It divides the price chart into horizontal zones and tracks which "level" the market currently occupies, helping traders diversify their positions across different price ranges rather than concentrating risk at a single strike. The indicator uses a playful Star Wars naming convention: upward-trending levels are called "Jedi Levels" (JL) and downward-trending levels are called "Sith Levels" (SL). This terminology originated from a trading mentor who found it easier to remember than directional abbreviations. How It Works Level Grid System The indicator creates a grid of horizontal price levels based on your chosen spacing (default: 10 points). Each level represents a price zone where you might consider placing a spread trade. Trend State Tracking The indicator operates in one of two modes: Jedi Mode (Bullish): When price is advancing upward through levels. Each time price breaks above the current level's top boundary, the indicator advances to the next Jedi Level (JL1 to JL2 to JL3, etc.). Sith Mode (Bearish): When price is declining through levels. Each time price breaks below the current level's bottom boundary, the indicator advances to the next Sith Level (SL1 to SL2 to SL3, etc.). Level Transitions Transitions between modes occur when price reverses and touches the opposing level boundary. The indicator uses high/low touches (not closes) to determine level breaks, providing faster signals. Trade Visualization Boxes You can overlay up to 10 colored rectangles representing your actual options positions. Each box shows: - Opening date (when you entered the trade) - Expiration date (when the options expire) - Upper and lower strikes (defining your spread's range) - Custom label (e.g., "Jan IC" or "Feb Put Spread") This lets you see at a glance which price zones you have covered and where gaps exist in your "grill." Practical Application Vertical Diversification Strategy The core idea is to diversify iron condors across multiple price levels rather than placing all trades at the current market price: When market reaches extended Jedi Levels (JL3 or higher): Consider reducing delta on new put credit spreads, as the market may be overextended to the upside. When market reaches extended Sith Levels (SL3 or higher): Consider increasing delta on new positions, anticipating potential mean reversion. Coverage Visualization By drawing boxes for your active positions, you can see which price ranges are "protected" by existing spreads and identify gaps where additional positions might provide better coverage. Settings Guide Main Settings Level Spacing - Distance between horizontal levels in price points. Default is 10. For SPY, 10 points creates meaningful zones; for SPX, consider 50-100 points. Trade Boxes (1-10) Each trade slot has these settings: Show Trade - Toggle visibility of this position box Label - Custom name for the trade (e.g., "Jan 17 IC") Opening Date - When you entered the position Expiration Date - Options expiration date Upper Strike - Top of your spread range Lower Strike - Bottom of your spread range Visual Elements Green labels (JL1, JL2...) - Mark upward level progressions Red labels (SL1, SL2...) - Mark downward level progressions Blue labels - Mark trend reversal points (JL1 after Sith mode, SL1 after Jedi mode) Dashed blue grid lines - Show level boundaries extending into the future Colored boxes - Your configured trade positions Status table (top right) - Current price, level, and trend direction What Makes This Different Unlike standard support/resistance indicators, BBQ Levels is specifically designed for options spread traders. It provides: A systematic framework for diversifying positions across price levels Visual overlay of actual trade positions against the level grid State-based tracking that distinguishes between bullish and bearish market phases Actionable context for adjusting spread deltas based on market extension Best Used On SPY, SPX, or other index products where you trade iron condors Daily or 4-hour timeframes for position planning Lower timeframes (1H, 15m) for timing entries within levels Limitations This indicator does not predict price direction - it only tracks which level price currently occupies The level spacing is fixed and does not adapt to volatility Trade boxes are manual inputs - you must update them as you open/close positions Level progression rules may generate frequent signals during choppy, range-bound markets This is a visualization and organizational tool, not a trading signal generator Disclaimer This indicator is for educational and organizational purposes only. It does not constitute financial advice and should not be used as the sole basis for trading decisions. Options trading involves substantial risk and is not suitable for all investors Past performance does not guarantee future results Iron condors and credit spreads have defined risk but can still result in significant losses Always conduct your own research and consider consulting a financial professional The author is not responsible for any trading losses incurred using this tool Version History v1.0 - Initial release with level tracking v1.1 - Bug fix: levels now update on touch, not close v1.2 - Added trade visualization boxes (up to 10 positions) v1.3 - Fixed expiration date rendering for trade boxes
Индикатор Pine Script®
от luiscaballero
Adaptive Quant RSI [ML + MTF]This is an advanced momentum indicator that integrates Machine Learning (K-Means Clustering) with Multi-Timeframe (MTF) analysis. Unlike traditional RSI which uses fixed 70/30 levels, this script dynamically calculates support and resistance zones based on real-time historical data distribution. Key Features: 🤖 ML Dynamic Thresholds: Uses K-Means clustering to segment RSI data into clusters, automatically plotting dynamic long/short thresholds that adapt to market volatility. ⏳ MTF Trend Background: The background color changes based on a Higher Timeframe (e.g., 5-min) RSI trend, helping you align with the broader market direction. 📊 Extreme Statistics: Incorporates percentile analysis (95th/5th) and historical pivots to identify extreme overbought/oversold conditions with high reversal probability. 📈 Probability Analysis: Displays the statistical probability of the current RSI value being at the top or bottom of its historical range. Usage: Look for confluence between the dynamic ML thresholds and the MTF background color to identify high-probability reversal setups.
Индикатор Pine Script®
от tkwatck
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.
Индикатор Pine Script®
от DskyzInvestments
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Percentage Level TargetsDisplays dynamic percentage-based price target levels at ±2.5% and ±5% from current price. ⭐ FEATURES: ✓ Real-time level updates on every candle ✓ Customizable label positioning (left/right) ✓ Adjustable offset for precise placement ✓ Works on ALL timeframes and assets ✓ Color-coded levels (green/red) 🎯 USE CASES: → Identify profit targets quickly → Set stop-loss levels automatically → Risk/reward ratio planning → Scalping & swing trading ⚙️ CUSTOMIZATION: • Adjust percentage levels (default: ±2.5%, ±5%) • Toggle labels on/off • Change colors for positive/negative levels • Control label position & offset 📊 COMPATIBLE WITH: Stocks • Crypto • Forex • Commodities All timeframes (1m, 5m, 1h, 4h, Daily, Weekly, Monthly) Feedback welcome! 🙌
Индикатор Pine Script®
от gcet